U.S. patent application number 13/205614 was filed with the patent office on 2012-02-09 for method and apparatus for generating an environmental element prediction for a point of interest.
Invention is credited to Carlos Repelli, Rodrigo Zerlotti.
Application Number | 20120035898 13/205614 |
Document ID | / |
Family ID | 37464545 |
Filed Date | 2012-02-09 |
United States Patent
Application |
20120035898 |
Kind Code |
A1 |
Repelli; Carlos ; et
al. |
February 9, 2012 |
METHOD AND APPARATUS FOR GENERATING AN ENVIRONMENTAL ELEMENT
PREDICTION FOR A POINT OF INTEREST
Abstract
An apparatus for generating environmental element predictions at
a point location includes a receiver for collecting broadcast
environmental element prediction data. A processor generates at
least one environmental element prediction for the point
location.
Inventors: |
Repelli; Carlos; (Austin,
TX) ; Zerlotti; Rodrigo; (Austin, TX) |
Family ID: |
37464545 |
Appl. No.: |
13/205614 |
Filed: |
August 8, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11140485 |
May 28, 2005 |
7996192 |
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13205614 |
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Current U.S.
Class: |
703/2 ;
703/6 |
Current CPC
Class: |
G01W 1/00 20130101; G01W
1/10 20130101 |
Class at
Publication: |
703/2 ;
703/6 |
International
Class: |
G06F 17/50 20060101
G06F017/50 |
Claims
1. An apparatus comprising: a receiver for collecting broadcast
environmental element prediction data associated with at least one
point location; and a processor processing the environmental
element prediction data to generate at least one environmental
element prediction for a point of interest, wherein the point of
interest is distinct from any point location associated with the
broadcast environmental element prediction data, wherein the
receiver and the processor are located at the point of
interest.
2. The apparatus of claim 1 wherein the environmental element
prediction data is at least one of grid prediction data and scatter
prediction data.
3. The apparatus of claim 1 wherein the environmental element
prediction data includes both grid prediction data and scatter
prediction data.
4. The apparatus of claim 1 wherein the environmental element
prediction data includes at least one element from the set
including {air temperature, pressure, wind speed, wind direction,
probability of precipitation, amount of precipitation, humidity,
cloud cover, visibility, radiation, wind chill, heat index, water
temperature, wave height, wave direction}.
5. The apparatus of claim 1 wherein the environmental element
prediction is for at least one element from the set including {air
temperature, pressure, wind speed, wind direction, probability of
precipitation, amount of precipitation, humidity, cloud cover,
visibility, water temperature, wave height, wave direction, heat
index, wind chill, drought index, soil moisture, ultraviolet
radiation, aerosol dispersion}.
6. The apparatus of claim 1 further comprising a locator for
providing the point of interest.
7. The apparatus of claim 6 wherein the locator determines the
point of interest through satellite trilateration.
8. The apparatus of claim 1 wherein the environmental element
prediction data is collected by the receiver from a wireless
over-the-air broadcast.
9. The apparatus of claim 1 wherein the environmental element
prediction data is collected by the receiver from a computer
network.
10. A method comprising: a) receiving broadcast environmental
element prediction data for at least one point location; and b)
computing at least one environmental element prediction for a point
of interest from the environmental element prediction data, wherein
the point of interest is distinct from any point location of the
environmental element prediction data, wherein the computing is
performed by a processor, wherein the receiver and the processor
are located at the point of interest.
11. The method of claim 10 wherein the environmental element
prediction data comprises at least one of grid data and scatter
data.
12. The method of claim 10 wherein the environmental element
prediction data comprises both grid data and scatter data.
13. The method of claim 10 further comprising: c) generating a
control signal for controlling a controlled element in response to
the at least one environmental element prediction for the point of
interest.
14. The method of claim 13 wherein the controlled element is a
fluid.
15. The method of claim 13 wherein the controlled element is
water.
16. The method of claim 13 wherein the controlled element is
electrical power.
17. The method of claim 13 wherein the controlled element is
temperature.
18. The apparatus of claim 10 wherein the received data includes at
least one element from the set including {air temperature,
pressure, wind speed, wind direction, probability of precipitation,
amount of precipitation, humidity, cloud cover, visibility,
radiation, wind chill, heat index, water temperature, wave height,
wave direction}.
19. The apparatus of claim 10 wherein the environmental element
prediction is for at least one element from the set including {air
temperature, pressure, wind speed, wind direction, probability of
precipitation, amount of precipitation, humidity, cloud cover,
visibility, water temperature, wave height, wave direction, heat
index, wind chill, drought index, soil moisture, ultraviolet
radiation, aerosol dispersion}.
20. A control apparatus comprising: a receiver collecting broadcast
environmental element prediction data associated with at least one
point location as received data; a processor processing the
received data to generate at least one environmental element
prediction for a point of interest, wherein the point of interest
is distinct from any point location of the received data, wherein
the receiver and the processor are located at the point of
interest, wherein the processor generates a first control signal
based upon the at least one environmental element prediction; and a
controller for controlling a controlled element, the controller
responsive at least in part to the first control signal.
21. The control apparatus of claim 20 further comprising: a least
one environmental sensor providing a second control signal, wherein
the controller controls the controlled element in accordance with
at least one of the first and second control signals.
22. The apparatus of claim 20 wherein the controlled element is a
fluid.
23. The apparatus of claim 22 wherein the fluid is water.
24. The apparatus of claim 20 wherein the controlled element is
electrical power.
25. The apparatus of claim 20 wherein the controlled element is
temperature.
26. The apparatus of claim 20 wherein the received data includes at
least one element from the set including {air temperature,
pressure, wind speed, wind direction, probability of precipitation,
amount of precipitation, humidity, cloud cover, visibility,
radiation, wind chill, heat index, water temperature, wave height,
wave direction}.
27. The apparatus of claim 20 wherein the environmental element
prediction is for at least one element from the set including {air
temperature, pressure, wind speed, wind direction, probability of
precipitation, amount of precipitation, humidity, cloud cover,
visibility, water temperature, wave height, wave direction, heat
index, wind chill, drought index, soil moisture, ultraviolet
radiation, aerosol dispersion}.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of prior application Ser.
No. 11/140,485 filed May 28, 2005 which is incorporated by
reference herein in its entirety.
TECHNICAL FIELD
[0002] This invention relates to the field of forecasting
environmental conditions. In particular, this invention is drawn to
the generation and use of environmental element predictions for a
point location.
BACKGROUND
[0003] Environmental conditions for a location or region can be
described with various environmental elements and their associated
value. The value of a given set of such environmental elements
defines the environmental state of interest for the location or
region. Accurate predictions of an environmental state are useful
for planning a wide range of activities for any number of entities
including government, military, consumer, and other commercial
enterprises. Groups of environmental elements are frequently
categorized for convenience into classifications such as
meteorological, marine, hydrological, etc.
[0004] Meteorological elements reflect a subset of environmental
elements that describe the physical and dynamic behavior of the
atmosphere. Weather is a description of short-term atmospheric
behavior. Weather predictions might be useful, for example, to
determine when to travel, a transportation route, or a mode of
transportation. Longer-term atmospheric behavior is generally
referred to as climate and falls within the field of climatology.
Climate prediction is useful for longer term planning such as
determining which crops to plant. Global models have been developed
to aid in studying and forecasting some environmental elements,
particularly meteorological elements and those environmental
elements pertaining to the determination of the meteorological
elements (e.g., hydrological elements).
[0005] Generating a global weather forecast requires tremendous
computational power. Typically, government-sponsored organizations
develop global weather models, collect and maintain data for the
models, and run the models to generate predictions about the
weather in a process referred to as numerical weather prediction.
Government sponsorship is prevalent due to the capital-intensive
nature of the computational resources involved, the volume and
source of the data required, and the benefits afforded on a
societal scale.
[0006] Numerical weather prediction involves numerically
integrating a set of differential equations. This is accomplished
by dividing the spatially relevant portion of the atmosphere into a
finite number of three dimensional grid elements and performing a
time-series finite element analysis. Due to the time constraints
and the computational resources available, the forecast is
typically a synoptic scale forecast having a spatial resolution on
the order of a hundred or more kilometers latitudinally and
longitudinally.
[0007] The global weather model becomes the starting point for
determining a weather forecast on a finer spatial resolution. For
example, local weather prediction is often handled by human
meteorologists local to the region at issue who rely on numerical
weather predictions, observation, history, and their own experience
for generating a regional weather forecast. The forecast generated
by the meteorologist usually covers a relatively large region
(e.g., city-wide, county-wide, etc.) and is typically designated
for local landmarks (e.g., downtown, airport, stadium, etc.).
[0008] One disadvantage of this approach is that a skilled
professional is required. The skilled professional typically only
address regions near populous areas, significant landmarks, or
observation stations.
[0009] Another disadvantage of this approach is that the results
are expressed for the entire region even though the weather
condition may vary greatly from one location to another within the
region. Temperatures at an airport or a city center, for example,
may be extraordinarily elevated when compared with temperatures
near a lake within the same region. Thus the regional forecasting
approach does not address the anticipated fluctuation in weather
that may occur from point to point within the same region.
SUMMARY
[0010] In view of limitations of known systems and methods, various
methods and apparatus for generating point environmental element
predictions are described. In one embodiment, an apparatus for
generating environmental element predictions at a point location
includes a receiver collecting broadcast environmental element
prediction data. A processor generates at least one environmental
element prediction for the point location.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The present invention is illustrated by way of example and
not limitation in the figures of the accompanying drawings, in
which like references indicate similar elements and in which:
[0012] FIG. 1 illustrates the layers of the atmosphere.
[0013] FIG. 2 illustrates numerical weather prediction
incorporating a human forecaster.
[0014] FIG. 3 illustrates numerical weather prediction
incorporating a forecast engine.
[0015] FIG. 4 illustrates three-dimensional grid elements of a
layered global weather model.
[0016] FIG. 5 illustrates one embodiment of a method of generating
environmental element predictions for a point of interest.
[0017] FIG. 6 illustrates one embodiment of mapping a
three-dimensional grid of environmental element prediction data to
a single layer two-dimensional grid.
[0018] FIG. 7 illustrates one embodiment of environmental element
prediction grid data overlayed with observation points associated
with environmental element observed data.
[0019] FIG. 8 illustrates one embodiment of a determination of
relevance of grid points and observation points for a specific
point.
[0020] FIG. 9 illustrates one embodiment of a method for generating
point environmental element prediction data for a specific point
using the environmental element prediction grid data.
[0021] FIG. 10 illustrates one embodiment of a data collection for
estimating prediction errors for an observation point.
[0022] FIG. 11 illustrates one embodiment of a network
environment.
[0023] FIG. 12 illustrates one embodiment of server processes
supporting generation of point environmental element prediction
data in a client-server network environment.
[0024] FIG. 13 illustrates one embodiment of a method of generating
point environmental element prediction data in a client-server
network environment.
[0025] FIG. 14 illustrates an alternative embodiment of a method of
generating point environmental element prediction data in a
client-server network environment.
[0026] FIG. 15 illustrates one embodiment of server processes
supporting generation of point environmental element prediction
data in a broadcast network environment.
[0027] FIG. 16 illustrates one embodiment of a method of generating
point environmental element prediction data by a receiving device
in a broadcast network environment.
[0028] FIG. 17 illustrates one embodiment of a method of collecting
broadcast data for a point of interest.
[0029] FIG. 18 illustrates one embodiment of an environmental
element prediction device (EEPD).
[0030] FIG. 19 illustrates another embodiment of an EEPD.
[0031] FIG. 20 illustrates one embodiment of a controller
incorporating a environmental element predictive sensor and
hydrological sensor for plant control.
[0032] FIG. 21 illustrates one embodiment of an integrated
hydrological sensor and environmental element predictive sensor
suitable for retrofitting a controller limited to a rain
sensor.
[0033] FIG. 22 illustrates one embodiment of a process implemented
by an EEPD.
[0034] FIG. 23 illustrates another embodiment of a process
implemented by an EEPD.
DETAILED DESCRIPTION
[0035] Environmental elements may also alternatively be referred to
as environmental variables, parameters, or conditions and generally
describe a physical characteristic or attribute of the environment
about a given location. Knowledge of the conditions or values for
these elements is important to determine whether and to what extent
the existence or development of someone or something will be
affected. These environmental elements are frequently categorized
into groups for ease of use, however, one element may be in more
than one category. "Meteorological" is one such category of
environmental elements. Meteorological elements will frequently be
utilized for purposes of example.
[0036] The envelope of air surrounding the Earth and bound to the
Earth by gravity is referred to as the atmosphere. The structure,
properties, and physical processes of the atmosphere are the
subject matter of the field of meteorology.
[0037] The term "climate" is generally used to refer to long-term
atmospheric behavior. "Weather" reflects the short-term state of
the atmosphere, particularly those characteristics that tend to
affect human activity. Weather, generally refers to variations of
the atmospheric state over periods of a few minutes to a few
weeks.
[0038] FIG. 1 illustrates various layers of the atmosphere. The
layer closest to the Earth is referred to as the troposphere 110.
The troposphere extends from the surface of the Earth to
approximately 8-15 km. The tropopause 120 separates the troposphere
from the stratosphere. The stratosphere 130 extends to
approximately 50 km before the stratopause 140 is encountered. The
stratopause is followed by the mesosphere 150 that extends to
approximately 85 km. The mesosphere is followed by the mesopause
160. Following the mesopause, the thermosphere 170 extends up to
600 km. The thermosphere is followed by the exosphere 180. Each of
these layers has distinct physical and chemical properties. Weather
is dictated predominately by the state of the troposphere 110.
[0039] The weather at a particular place and time may be
characterized by a number of meteorological elements. The elements
might include, for example: air temperature, pressure, wind speed,
wind direction, probability and amount of precipitation, humidity,
cloud cover, and visibility.
[0040] FIG. 2 illustrates one embodiment of a numerical weather
prediction (NWP) process incorporating a human forecaster. A model
250 representing the atmosphere is provided to processor 210.
Observed data 240 and historical data 220 serve to establish the
initial conditions for the model. The observed data is also
recorded into the historical data for future use. Processor 210
solves the aforementioned equations to calculate future states of
the atmosphere as model predictions 260. The model predictions are
also typically recorded as historical data for testing the validity
of the model.
[0041] A human forecaster 230 (i.e., meteorological expert familiar
with the local area of interest) interprets the model predictions
260, observed data 240, and the historical data 220 to generate an
inevitably subjective prediction 270. The human forecaster plays an
integral role in revising the model predictions to create
subjective predictions that incorporate the forecaster's personal
experience or familiarity with regional weather behavior.
[0042] An improved NWP process incorporates a forecast engine
rather than a human forecaster. FIG. 3 illustrates one embodiment
of a NWP process incorporating such a forecast engine. As with FIG.
2, the model 350 representing the atmosphere is provided to
processor 310. Observed data 340 and historical data 320 serve to
establish the initial conditions for the model. The observed data
is also recorded into the historical data for future use. Processor
310 solves the aforementioned equations to calculate future states
of the atmosphere as model predictions 360. The model predictions
are also recorded as historical data for testing the validity of
the model.
[0043] In contrast with FIG. 2, a forecast engine 330 is used to
generate objective predictions 370 from the observed data 340,
historical data 320, and model predictions 360. Although FIG. 3 is
drawn to numerical weather prediction, such a forecast engine can
likewise be used to provide environmental element predictions
without the human forecaster.
[0044] Various aspects of a forecast engine including forecasting
processes, system architectural implementations (e.g.,
client-server, broadcast, etc.), physical implementation (e.g.,
client computer application, stand-alone device, etc.) as well as
practical applications (e.g., irrigation control) are described
below.
[0045] Numerical weather prediction relies upon a meteorological
model of the atmosphere to approximate the behavior of the
atmosphere over time. Several models for NWP are available. The
models typically divide the spatially relevant portion of the
atmosphere into a finite number of grid elements.
[0046] The NWP model incorporates equations from fluid dynamics
including equations of motion, thermodynamic and moisture
equations, and the continuity equation for conservation of mass for
each grid element. The equations are then solved in time steps to
calculate future states of the atmosphere as a regular grid of
meteorological element prediction data. Initial conditions for the
grid points are established by interpolation from meteorological
element data observed and reported from various observation points.
The solution to the aforementioned equations is iteratively derived
using the interpolated observed meteorological element data for the
initial conditions. The model may be run several times a day as the
observations are updated.
[0047] FIG. 4 illustrates a portion of the atmosphere partitioned
into a plurality of layers of brick-shaped grid elements. Each grid
element 402 has an associated X, Y, Z co-ordinate. Thus, the
troposphere may be subdivided into additional layers 410-420 of
grid elements for NWP. The result of NWP can be described as an
array of one or more meteorological elements such as temperature,
humidity, pressure, etc. for each grid point associated with a grid
element. Each array is associated with a specific time or time step
and a specific X, Y, Z co-ordinate location. The term "prediction"
may include future timeframes and the current timeframe (i.e., time
step or timeframe 0).
[0048] Layers 410-420 of grid elements 402 become layers 460-470 of
grid points 452. The distance between the grid points is referred
to as the grid length. The grid length is representative of spatial
resolution. Vertical levels determine the vertical resolution of
the model. The result of NWP is thus a three-dimensional grid of
points 452 each of which is associated with an array of
meteorological elements at a specific time or time step. The points
are referred to as a grid points. The information (e.g., location,
environmental element prediction data) associated with one or more
such points is collectively referred to as grid data.
[0049] The size of the grid elements determines the spatial
resolution of the NWP. Dimensions measured about the surface of the
earth (i.e., same radius) are referred to as horizontal. The
horizontal grid length WX may be distinct from the horizontal grid
length WY. Dimensions measured perpendicular to the surface of the
earth are referred to as vertical or layer. The grid element height
is WZ and defines the vertical resolution for the model.
[0050] Due to the time constraints (the prediction must be
available before the targeted time frame for practical use) and the
computational resources available, the prediction is typically a
synoptic scale prediction with each grid element having a spatial
resolution on the order of hundreds of
kilometers)(1.degree..times.1.degree. for a global model or around
20 km by 20 km for a regional model. A NWP model utilizing 10-40
vertical layers is not unrealistic. The computational power
required to perform global predictions with so many elements is
significant. Generally only very powerful supercomputers are
capable of performing this task within the timeframe
limitations.
[0051] Collectively, the grid elements can provide information
about weather phenomena operating across areas that span multiple
grid elements. NWP cannot resolve meteorological element details
within an individual grid element. At best, aggregate inferences
may be made about the meteorological elements within the grid
elements.
[0052] Several meteorological models are available for predicting
the weather. Examples include North American Mesoscale and variants
(NAM, NAM-NMM, NAM-WRF, ETA), Global Forecast System (GFS), Rapid
Update Cycle (RUC), Air Force Weather Agency Mesoscale Model
version 5 (AFWA/MM5), Navy Operational Global Atmospheric
Prediction System (NOGAPS), Coupled Ocean/Atmosphere Mesoscale
Prediction System (COAMPS), and Global Environmental Multiscale
(GEM). Some additional global models include Medium Range Forecast
(MRF), and Aviation Model Forecast (AVN). Regional models include
the ETA model, Regional Atmospheric Modeling System (RAMS), and
Mesoscale Model 5 (MM5).
[0053] The models may be differentiated on a number of factors
including the organization or entity managing and operating the
model, the scale (regional, mesoscale, global), model structure
(e.g., number of vertical layers, horizontal resolution, etc.),
model physics (parameterization for precipitation, clouds,
radiative processes, etc.), the manner in which various physical
processes are approximated, and the approximations made when
numerically solving the equations governing the physical
processes.
[0054] The World Meteorological Organization (WMO) has established
a World Weather Watch (WWW) program to ensure members obtain the
appropriate weather data for operational and research purposes. The
WWW program includes a global observing system (GOS), a global
data-processing system, and a global telecommunication system
(GTS).
[0055] The GTS is a co-ordinated global system of telecommunication
facilities that support the rapid collection, exchange and
distribution of observational data in the framework of the WWW. GOS
is a global network of observational stations and a coordinated
system of methods, techniques and facilities for making
observations on a world-wide scale in the framework of the WWW. For
reasons to be described later, it is important to realize that the
observational data provided to the NWP processor does not
necessarily correspond to or nicely overlay the three-dimensional
grid of elements used in the NWP meteorological model. For example,
there may be large geographic areas for which no observations are
available. In other geographic areas, the observation points may
not be well distributed such that there is considerable information
for some regions and little information for other regions in
proximity.
[0056] Although the grid data may be readily available, the grid
data does not reflect the large variations in weather that might be
experienced within any given region. In other words, the grid data
might reflect net predictions for the entire region without being
particularly representative of any specific point within the
region. More accurate weather prediction data for specific points
within a given region is highly desirable.
[0057] U.S. Pat. No. 6,823,263 of Kelly, et al. ("Kelly") discloses
subdividing a grid element having a coarse spatial resolution into
a plurality of grid elements having a much finer spatial
resolution. The NWP techniques previously described may be used to
generate weather predictions at the finer spatial resolution. As
noted by Kelly, however, this still requires significant
computational power.
[0058] Although Kelly's approach might be useful for a few specific
pre-determined points, any other point would be underserved. The
prediction associated with one of the pre-determined points may
become highly unreliable even a short distance from the
pre-determined point.
[0059] An alternative approach is to use a less computationally
intensive numerical approach guided by the grid data (i.e., coarse
spatial resolution prediction data) to generate predictions for
either pre-determined sites or user-selected sites. This approach
may be applied to predict environmental elements generally and is
not otherwise limited to meteorological elements.
[0060] FIG. 5 illustrates one embodiment of a method of generating
environmental element prediction data for a point of interest.
Environmental element grid data is collected in step 510. The
environmental element grid data has a first spatial resolution
defined by a first grid length. Typical grid lengths are 20 km-120
km.
[0061] In step 520, environmental element observation data is
collected. The observation data for a given observation point is
the actual environmental element data measured at that site. Thus
the observation data for a given observation point may comprise a
latitude, longitude, altitude, timestamp (i.e., date and time) and
any number of environmental elements observed values (e.g.,
temperature, precipitation, etc.)
[0062] Point environmental element predictions are interpolated for
the observation points using the grid data in step 530. A
prediction error characterizing the error between the environmental
element predictions interpolated from the grid data and the actual
observed values at the observation points is generated in step
540.
[0063] A point environmental element prediction for a point of
interest is generated in step 550. The prediction error from one or
more observation points is used to generate a corrected point
environmental element prediction in step 560.
[0064] Referring to step 510, only grid data from one altitude at
any location is collected in one embodiment. Thus for any X, Y
cell, only one grid point along the Z-axis is selected. In one
embodiment, the grid point associated with the layer or level
closest to the surface is selected. The level selected is thus
dependent upon the altitude of the topographical features of the
location. Referring to FIG. 3, any X, Y grid location has a
plurality of grid points in a vertical column because of the
multiple layers of the model. Only one of the layers along the
Z-axis is chosen for any given X, Y cell, and the choice is based
on the proximity of the layer to the surface as determined by the
Earth's topographical features.
[0065] FIG. 6 illustrates mapping a three-dimensional set of grid
points to a horizontal two-dimensional grid. Grid data from a
selected layer at any X, Y location from three-dimensional grid 600
is mapped to a two-dimensional horizontal grid 650. The Z-axis
(i.e., altitude) information is preserved with each grid point
mapped into the two-dimensional grid so that the altitude of the
source of each grid point in two-dimensional grid 650 is known.
[0066] In one embodiment, the layer or level closest to the
topographical surface at a particular X, Y location is the layer
from which a grid point will be selected for that X, Y location. In
some grids, the grid point might reside within the center of a grid
cell. In the illustrated embodiment, the grid points are located at
the corners of a grid cell and thus may be shared by 4 grid cells.
The grid point closest to the ground is chosen.
[0067] Referring to three-dimensional grid 600, the grid points
that meet this qualification include 610-618. Referring to
two-dimensional grid 650, the source grid point mapping from
three-dimensional grid 600 to each selected grid point 660-668 of
two-dimensional grid 650 is as follows: 660.rarw.610, 661.rarw.611,
662.rarw.612, 663.rarw.613, 664.rarw.614, 665.rarw.615,
666.rarw.616, 667.rarw.617, and 668.rarw.618. The result of step
510 is a collection of grid data based on a coarse spatial
resolution of grid length WX, wherein the grid point at a specific
X, Y location has an associated altitude Z and prediction data
specific to the grid point.
[0068] FIG. 7 illustrates an overlay of the relative locations of
environmental element observation data with the environmental
element grid data. The density and distribution of the observation
sites or observation points can be expected to vary significantly.
Most importantly, the observation points 720 do not necessarily
correspond to any grid point locations 710. Due to the lack of
regularity of distribution of the observation points, these points
and the data associated with them may generally be referred to as
scatter points or scatter data.
[0069] FIG. 8 illustrates one embodiment of a collection of
environmental element grid data and the environmental element
observation data. With respect to a specific point 810, the data
associated with locations that are closer to the point of interest
is presumed to be more representative of the expectations at the
point of interest 810 than data that is further away from the point
of interest. The proximity of the location associated with the grid
data to the specific point is determinative of the relevance of the
grid data. Thus, for example, with respect to grid points 830 and
840, grid point 830 is presumed to more accurately reflect the
expected conditions at point 810.
[0070] Similarly, the proximity of observation points to the point
of interest is determinative of the relevance of the observation
data. Thus, for example, with respect to observation points 820,
822, 823, and 850, the data associated with observation points 820,
822, and 823 is presumed to be more relevant than the data
associated with observation point 850.
[0071] FIG. 9 illustrates one embodiment of a method of generating
point environmental element prediction data for a selected point
from grid points. At least one grid point adjacent the selected
point of interest is selected in step 910. Preferably a plurality
of adjacent grid points are selected. In one embodiment, at least
four grid points distributed about the point of interest are
selected. Generally, the "nearest" grid points are the most
relevant. In the graphical illustration, grid points 952, 954, 956,
and 958 are the grid points adjacent selected point 950. In the
event that the co-ordinates of the selected point match those of a
grid point, that grid point is sufficient and no additional
relevant grid points need to be identified.
[0072] A point environmental element prediction is generated from
the selected grid points in step 920. In one embodiment, the point
environmental element prediction is interpolated using a weighted
average of the grid points. The weighting is based on the relative
distances between the selected point and each selected grid point
with closer grid points weighted more heavily than distant grid
points. In the event that the co-ordinates of the selected point
match those of a grid point, the grid data associated with that
single grid point may be used without interpolation.
[0073] In one embodiment, the weighting is inversely related to
distance by an exponential function of the form e.sup.-kd.sup.2,
where d corresponds to the distance between the selected point and
the selected grid point, where k is a factor that may vary for each
environmental element parameter (i.e., k may have one value for
temperature and another value for precipitation). An interpolated
environmental element value for a parameter, E, based solely on
distance from relevant grid points might be calculated as
follows:
E INT = i = 1 n - kd i 2 E i i = 1 n - kd i 2 ##EQU00001##
[0074] where d.sub.i is the X,Y distance between the selected point
and the i.sup.th selected grid point (e.g., D.sub.952, D.sub.954,
D.sub.956, D.sub.958); E.sub.i is the value of the environmental
element of interest at the i.sup.th grid point; and E .sub.INT is
the interpolated environmental element value.
[0075] In order to ensure the most relevant grid points are
selected and computational simplicity, a threshold operation may be
performed. The threshold operation, for example, may examine any of
d, d.sup.2, kd.sup.2 to determine whether a given corresponding
pre-determined value is exceeded. Alternatively, the threshold
operation may determine whether e.sup.-kd.sup.2 is less than a
pre-determined value.
[0076] The selected grid points may be at different altitudes than
the selected point. For some environmental element predictions, an
altitude correction may be appropriate. Thus an altitude correction
is performed in step 930, if necessary. As with the horizontal
displacement, the vertically closer grid points are expected to be
more relevant than the more distant grid points.
[0077] In one embodiment, the altitude correction is based on the
difference between the altitude of the selected point and an
interpolated altitude from the selected grid points. An
interpolated altitude may be calculated in any number of ways. In
one embodiment, the interpolated altitude is calculated as
follows:
A INT = i = 1 n - kd i 2 A i i = 1 n - kd i 2 ##EQU00002##
[0078] where A.sub.i is the altitude of the i.sup.th grid point.
Thus the interpolated altitude may be computed in the same manner
as the interpolation for any environmental element.
[0079] The varying of a environmental element such as temperature
with altitude is referred to as "lapse rate". Lapse rate may be
used for performing altitude corrections for some environmental
elements (e.g., temperature). The lapse rate may change throughout
the course of a day and from point to point. The change in
temperature from one altitude (a1) to another (a2) can be
determined by the following:
.DELTA.T=.intg..sub.a1.sup.a2L(a)da
where L(a) is the lapse rate.
[0080] FIG. 1 illustrates one embodiment of a lapse rate 112 of
temperature through various layers of the atmosphere. An observed
or modeled lapse rate specific to the location of the point of
interest may be used. Alternatively, a standardized lapse rate may
be used irrespective of location. In some cases, the lapse rate
over a particular range of altitudes may be approximated as a
constant, L.
[0081] In order to perform the altitude correction for temperature,
the following equation may be applied:
T.sub.P=T.sub.INT+.intg..sub.A.sub.INT.sup.A.sup.PL(a)da
where T.sub.P is the temperature at the selected point, T.sub.INT
is the interpolated temperature for the interpolated altitude, and
L(a) is the lapse rate in the altitude range from the interpolated
altitude (A.sub.INT) to the altitude (A.sub.P) of the selected
point. In the event that a standardized constant lapse rate is
applicable, the equation becomes:
T.sub.P=T.sub.INT+L(A.sub.P-A.sub.INT)
[0082] The point environmental element prediction is not limited to
a specific number of grid points. Although accuracy and
computational resource requirements may vary depending upon the
number and location of selected grid points, any number of grid
points may be used. In one embodiment, at least four grid points
distributed about the selected point (e.g., a point of interest)
are used (e.g., at least one grid point located in each quadrant
(Q1-Q4) of a Cartesian plane having co-ordinate axes with an origin
centered upon the selected point). The Cartesian plane is the
two-dimensional horizontal plane of grid points described
above.
[0083] Referring to FIG. 5, the method of FIG. 9 may be applied to
generate a point prediction for the point of interest (step 550) as
well as one or more observation points (530).
[0084] The point prediction for the observation point(s) is useful
for estimating prediction errors. Actual observed values can be
compared with the values that were previously predicted to
determine corrections that would have been needed for the
previously predicted values. Although the actual error in the
prediction is not known until the time period of interest has
passed, the use of standard statistical techniques such as
regression analysis may be used in conjunction with the historical
error to estimate the future prediction errors.
[0085] FIG. 10 illustrates a table containing the data to be
collected to permit estimating the prediction error associated with
a given observation point for a selected environmental element
(i.e., maximum temperature). Table 1010 includes columns for date,
observed, and 0-day, 1-day, etc. to N-day lagging predicted values.
The data corresponds to maximum temperatures associated with
observation point RZ1 at 30.1.degree. N 30.1.degree. W and 28 feet
above sea level for the indicated dates.
[0086] Each day, a maximum temperature forecast is calculated from
the relevant grid points for up to N days in advance. These maximum
temperatures were interpolated from relevant grid points using the
process of FIG. 9. The value in the x-day column for a given date
reflects the maximum temperature that was predicted x days prior to
the given date. Thus, for example, value 1012 indicates that the
maximum temperature predicted for date May 2, 2005 on May 2, 2005
was 86.degree. (when x=0, the value is the value calculated on the
same date). Value 1014 indicates that the maximum temperature
predicted on May 5, 2005 for May 7, 2005 (i.e., a 2-day forecast on
May 5, 2005) was 78.degree.. The predicted values are lagging
because they refer to predictions made on preceding dates about a
subsequent date.
[0087] Table 1050 illustrates the historical error by date to be
used for estimating the prediction errors for a given environmental
element. The prediction error for the 1-day prediction may be
different from the prediction error for the N-day prediction. Thus
the prediction errors may be grouped by columns (columns 1052-1058)
to permit separate estimations of the prediction errors (i.e.,
estimation of the 1-day prediction error distinct from the
estimation of the 2-day prediction error, etc.) The prediction
errors may also vary amongst different environmental elements such
that the error for each environmental element must be distinctly
tracked.
[0088] Referring to table 1050, the prediction error for a given
environmental element E.sup.m, at prediction observation point i,
prediction timeframe t, on a given date j is calculated as
ERR.sub.i.sup.m,t,j=E.sub.i.sup.m,t,j observed-E.sub.i.sup.m,t,j
predicted
[0089] The actual prediction error is clearly only known after the
time period of interest has passed. Once a sufficient history of
predicted and observed data is collected, various statistical
techniques (e.g., linear regression, average, etc.), other
mathematical techniques, or even artificially intelligent
approaches (e.g., neural networks) may be used on the historical
prediction errors to estimate the current prediction error for each
prediction timeframe (e.g., one-day, two-day, etc.). In various
embodiments 30-60 days of errors are collected for each observation
point and each environmental element. The result is that an
estimated error of the form EST_ERR.sub.i.sup.m,t,j may be
generated from the historical data for each environmental element
m, observation point i, prediction timeframe t, and given date j.
Given that the most current estimate is used for correction, the
date j is omitted for clarity (i.e., EST_ERR.sub.i.sup.m,t).
[0090] Thus for any given date, each observation point may have a
prediction error associated with each prediction timeframe. These
prediction errors are used to correct the corresponding
interpolated prediction from the grid data for any point of
interest using an estimated prediction error.
[0091] In one embodiment, the prediction error used to correct a
point prediction is estimated from the prediction errors associated
with relevant observation points. In particular, the prediction
error EST_ERR.sub.INT for the point of interest may be interpolated
from one or more observation points (i) as follows
EST_ERR INT m , t = i = 1 n - kd i 2 EST_ERR i m , t i = 1 n - kd i
2 ##EQU00003##
where d.sub.i is the distance between the observation point and the
point of interest.
[0092] As with the earlier calculations, k is a co-efficient that
may be derived from experience. The co-efficient k may be different
for different environmental elements as well as for different
prediction time frames (i.e., the prediction error for 1 day in
advance may utilize a different k (k1) than a prediction error for
2 days in advance (k2) such that k1.noteq.k2. Moreover the k1 for
temperature may be different than the k1 for precipitation).
[0093] In order to simplify computations for any of the
interpolations a subset of the observation points or grid points
may be selected based on relevancy. Thus, for example, a threshold
operation such as determining whether e.sup.-kd.sup.r.sup.2is less
than a pre-determined threshold may be used to determine whether
the r.sup.th observation point or grid point (as the case may be)
should be included in the computation.
[0094] The estimated error for the point of interest is thus the
estimated prediction error interpolated from the observation
points. This information is used to predict the point of interest's
environmental element values at various prediction timeframes as
follows:
E.sub.P.sup.m,t=En.sub.INT.sup.m,t-EST.sub.--ERR.sub.INT.sup.m,t
where E.sub.P.sup.m,t is the predicted value for environmental
element m and prediction timeframe t at the point of interest.
[0095] The number and category of environmental elements to be
predicted may vary upon the intended application. Air temperature,
pressure, wind speed, wind direction, probability and amount of
precipitation, humidity, cloud cover, and visibility are a subset
of environmental elements generally grouped as meteorological
elements. Marine elements might include water temperature, wave
height, wave direction, etc. Although the specific calculation for
a given environmental element might vary among elements, the
methods and apparatus described are not intended to be limited to a
pre-determined set of environmental elements. Examples of other
environmental elements might include heat index, wind chill,
drought index, soil moisture, levels of ultraviolet radiation,
aerosol dispersion, etc.
[0096] Centralization of the storage and maintenance of the
environmental element prediction grid data and the observed
environmental element data is one practical approach for supporting
a number of users who may be interested in environmental element
prediction data for various locations.
[0097] FIG. 11 illustrates a network environment including a
communication network 1110. Although the network may be an
"intranet" designed primarily for access between computers within a
private network, in one embodiment network 1110 is the network
commonly referred to as the Internet. The Internet includes a
combination of routers, repeaters, gateways, bridges, and
communications links with computers spread throughout the world.
The Internet facilitates communication between computers or other
devices connected to the Internet.
[0098] Some of the computers are referred to as "host" or "server"
computers because they provide services upon request. The computers
issuing the requests are referred to as "client" computers. The
network environment of FIG. 11 includes multiple (N) client
computers (1120, 1130, 1140) and multiple (M) host computers (1150,
1160, 1170). In some cases, a plurality of computers (e.g., 1130,
1140, 1150) may reside on a common network that shares a common
connection (e.g., via router 1180) to the Internet. The connection
between the client computer and the host may include wireless
links. Thus handheld devices such as cellular phones, personal
digital assistants, etc. (1182) may be client computers or in some
cases servers or hosts.
[0099] The host computers (e.g., 1150) and client computers (e.g.,
1120) can be entirely different architectures, however, to
facilitate communication on network 1110 they communicate by using
a common communication protocol. In one embodiment, this protocol
is the Transmission Control Protocol/Internet Protocol
(TCP/IP).
[0100] The client computers can request services from a host
computer. Hosts typically support file retrieval services, search
services, communication services, and recreational services. A
subset of Internet host computers provide multimedia information
services. This subset of host computers permit physical access to
the abstract body of information referred to as the World Wide Web
(WWW) and are referred to as WWW hosts or WWW servers.
[0101] World Wide Web host computers support a protocol that
permits users with computers having different architectures,
operating systems, and application programs to share multimedia
enhanced documents. In one embodiment, this protocol is the
Hypertext Transport Protocol ("HTTP"). The multimedia-enhanced
documents are often referred to as "web pages." The application
specific to a given hardware platform that permits viewing the web
pages is often referred to as a browser.
[0102] Uniform Resource Locators (URLs) provide a standard way of
referencing Internet resources including web resources. A URL
identifies the protocol as well as the location of the item to be
retrieved. The URL is not limited to other World Wide Web sites and
may in fact refer to other Internet protocols and services such as
Gopher, WAIS, UseNet news, Telnet, or anonymous FTP (file transfer
protocol).
[0103] A browser can access a host machine identified by the URL
and then retrieve the resource specified by the URL. The resource
identified by the URL may be static or dynamic. A static resource
is a resource that exists prior to the request and is simply
provided upon request. Examples of static resources include
document, image, movie, sound, or static web page files. Dynamic
resources are generated upon request and typically require some
type of information from the user (e.g., a database query requires
search parameters).
[0104] Consider the following URL:
[0105] http://www.infoweather.com/weather?LAT=30.1&LON=30.1
[0106] This URL identifies the protocol as "http" ("Hypertext
Transport Protocol"). The portion "www.infoweather.com" is an
Internet host address or symbolic representation of an Internet
host address. Thus "www.infoweather.com" identifies a specific
host. The portion of the URL identifying the specific host is often
referred to as a web site. The remainder is a path for the resource
that is being accessed. In the example above, the URL causes the
application "weather" to execute with the parameters LAT=30.1 and
LON=30.1 for the purpose of dynamically generating a web page
containing weather related information at that geographic location.
This dynamically generated web page may then be presented to the
requesting client effectively permitting a client to request and
receive environmental element predictions for a specified point
location.
[0107] FIG. 12 illustrates one embodiment of a server process 1200
in a client-server network environment. Environmental element
prediction grid data is collected in step 1210. Environmental
element observation data is collected in step 1220. Point
environmental element prediction data is generated for the
observation points in step 1230 using the environmental element
prediction grid data. In step 1240, prediction errors for the
observation points are calculated by comparing the point
environmental element prediction data with the environmental
element observation data for the observation point. The process may
be repeated to maintain an updated collection of prediction errors
and grid data.
[0108] FIG. 13 illustrates one embodiment of a method of generating
environmental element prediction data in a client-server network
environment. The dotted line provides a demarcation for the process
steps performed by the client versus those performed by the server
or host.
[0109] The client communicates a request for environmental element
prediction data at a specified point of interest to a server in
step 1342. The point of interest may be specified manually or
automatically. A GPS locator, for example, may be used to
automatically determine the location of the client and said
location is used as the specified point of interest. The advantage
of manual entry, however, is that locations other than the location
of the client may be specified.
[0110] The server receives the request for environmental element
prediction data for the specified point of interest in step 1344.
The server generates the point environmental element prediction
data for the point of interest from the environmental element
prediction grid data in step 1350. Corrected point environmental
element prediction data for the point of interest is generated in
step 1360 using the prediction error associated with one or more
selected observation points.
[0111] The server communicates the corrected point environmental
element prediction data for the point of interest to the client in
step 1362. If corrections are not necessary or desired, then step
1360 may be omitted. The client receives the environmental element
prediction data (corrected or uncorrected) for the specified point
of interest in step 1364.
[0112] The method of FIG. 13 is well-suited for clients that have
little computational ability. The maintenance of the grid data,
observation data, predicted error data and the computation of the
point environmental element prediction data are all handled by the
server.
[0113] One disadvantage of the method of FIG. 13 is that the
computational load and contention for the server increases with the
increase in client requests. FIG. 14 illustrates an alternative
embodiment of the client-server model that places more of the
computational load on the client.
[0114] The client communicates a request for environmental element
prediction data at a specified point of interest to a server in
step 1442. The point of interest may be specified manually or
automatically. A GPS locator, for example, may be used to
automatically determine the location of the client and said
location is used as the specified point of interest. The advantage
of manual entry, however, is that locations other than the location
of the client may be specified.
[0115] The server receives the request for environmental element
prediction data for the specified point of interest in step 1444.
The server communicates environmental element prediction grid data
and observation point prediction error data to the client in step
1446. In one embodiment, the server provides the data without
regard to the point location. In an alternative embodiment, the
server exclusively makes the determination of relevancy when
selecting grid point and observation point prediction error to
communicate to the client. In yet another embodiment, the server
provides data for a region relevant to the point of interest from
which the client may select a proper subset. This last embodiment
provides a reasonable trade-off between communication bandwidth and
supporting client discretion in determining relevancy of grid and
prediction error data.
[0116] The client generates the point environmental element
prediction data for the point of interest using environmental
element prediction data from selected grid points in step 1450. As
noted above, the selection of grid points may be determined
exclusively by the server, exclusively by the client, or
collectively by both the server and client in various embodiments.
The client, for example, may select a proper subset of the grid
points provided by the server.
[0117] The client generates corrected point environmental element
prediction data for the point of interest in step 1460 using the
prediction error associated with one or more selected observation
points. Selection of observation points may likewise be determined
exclusively by the server (by strictly limiting data provided),
exclusively by the client (i.e., client receives all data from
server), or collectively by both the server and client in various
embodiments. If corrections are not necessary or desired, then step
1460 may be omitted.
[0118] The client-server approach of FIG. 14 places a greater
computational burden on the client. Although the server load may be
reduced on an individual client basis compared to the process of
FIG. 13, the server load still inherently increases with the number
of clients. This approach may become undesirable as the number of
clients or the amount of data transferred per client increases. In
addition, bi-directional communication is inherently required for
the client-server architecture.
[0119] An alternative broadcast approach eliminates the contention
for the server as well as the requirement for bi-directional
communication with the server. The term "broadcast" is generally
characterized as a communication from a transmitter to one or more
receivers. In a classic broadcast environment (e.g., over-the-air
broadcast television, radio, satellite broadcast, etc.), the
transmission is unidirectional and the broadcaster has no knowledge
of the identity or number of receivers receiving the broadcast. Any
receiver within the coverage area of the transmitter can receive
the broadcast.
[0120] More recent broadcasting techniques (e.g., NARROWCAST,
POINTCAST, UNICAST, ANYCAST, MULTICAST, etc. such as might be used
in a computer network environment) permit specifying a group of one
or more intended recipients. As with the classic broadcast
environment, these more recent broadcasting techniques do not
require bi-directional communication with the receivers. The
information is transmitted substantially simultaneously to all
members of a specified group of two or more intended recipients
(individual recipients might ultimately receive the broadcast
information at different times depending upon different latencies
within the network topology).
[0121] FIG. 15 illustrates one embodiment of server processes
supporting generation of point environmental element predictions in
a broadcast network environment. The broadcast process of FIG. 15
is similar to the client-server process of FIG. 13 with the
requisite steps added for broadcasting the data.
[0122] The broadcast server collects the environmental element
prediction grid data in step 1510. The environmental element
observation data is collected in step 1520. Point environmental
element prediction data is generated for the observation points
using the environmental element prediction grid data in step 1530.
Prediction errors for the observation points are calculated in step
1540. The prediction error is determined from the generated point
environmental element prediction data and the environmental element
observation data. The environmental element prediction data is
broadcast in step 1580. The prediction error for the observation
points is broadcast in step 1582.
[0123] FIG. 15 is intended to represent an overall process flow,
however, various steps or collections of steps may be performed
concurrently or in a different order than what is illustrated. For
example, steps 1510 and 1530 may be performed concurrently with
step 1520. Similarly, steps 1580-1582 may broadcast data resulting
from one iteration of steps 1510-1540 concurrently with the
subsequent iteration of steps 1510-1540.
[0124] FIG. 16 illustrates one embodiment of a method of generating
point environmental element predictions by a receiving device in a
broadcast network environment.
[0125] Broadcast environmental element prediction grid data is
collected in step 1610. Broadcast observation point prediction
error is collected in step 1620. Point environmental element
prediction data for a point of interest is generated in step 1630
using selected environmental element prediction grid data.
Corrected point environmental element prediction data for the point
of interest is generated in step 1640 using the prediction error
from selected observation points.
[0126] The broadcast server may broadcast the grid data and
prediction error data grouped by geographic regions. Only a
selected few of the observation points and grid points are relevant
to the calculation of environmental element prediction data at the
point of interest. The receiving device must select the relevant
observation points and grid points. As previously indicated, a
threshold operation based on distance may be used to determine
whether particular grid points or observation points are
relevant.
[0127] FIG. 17 illustrates one embodiment of a method for
collecting the relevant broadcast data. In one embodiment, the
observation point prediction error data and the environmental
element prediction grid data is grouped and broadcast by region.
With this approach, a broadcast recipient or receiver need only
handle the data associated with the relevant region rather than
analyzing all broadcast data for a determination of relevance. The
broadcast data is collected based on its relevance to the point of
interest.
[0128] For computational efficiency, step 1710 indicates waiting
for the broadcast of data associated with a region (i.e., "region
of interest") containing the point of interest. Once the data for
the region of interest is broadcast, the data collection for the
point of interest may begin.
[0129] In step 1720, broadcast data associated with a given site is
received as a collected site. In step 1730, broadcast data
associated with the given site is optionally categorized.
Categorization permits subsequent filtering based on various
relevance criteria. For example, if environmental element grid data
or prediction error data from sites distributed about the point of
interest are desired, the given site may be categorized by its
quadrant relative to the point of interest. In one embodiment, the
quadrants are defined as follows:
[0130] quadrant 1: 0.degree..ltoreq..alpha.<90.degree.
[0131] quadrant 2: 90.degree..ltoreq..alpha.<180.degree.
[0132] quadrant 3: 180.degree..ltoreq..alpha.<270.degree.
[0133] quadrant 4: 270.degree..ltoreq..alpha.<0.degree.
[0134] The device receiving the broadcast data necessarily has a
finite memory. In order to ensure that the most relevant data is
considered given the constraints of the device, step 1740
determines if the number of any collected sites within the same
category as the given site exceeds a pre-determined threshold. If
so, a selected collected site that is further from the given site
yet in the same category as the given site is eliminated in step
1750. Within each category, the most relevant data (as determined
by distance from the point of interest) is retained.
[0135] After eliminating less relevant data (if necessary), step
1760 determines whether the broadcast of data associated with the
point of interest is completed. If not, steps 1720-1760 are
repeated until the regional broadcast is completed. If all the data
for the region of interest has been broadcast, the process returns
to step 1710 to wait until the next broadcast of data for the
region of interest.
[0136] The process of FIG. 17 may be used to collect environmental
element prediction grid data and prediction error data most
relevant to the point of interest. Once all the information for a
given region is broadcast, the collected data may be used to
generate corrected point environmental element prediction data for
the point of interest.
[0137] FIG. 18 illustrates one embodiment of a device 1810 for
receiving broadcast data and generating a point environmental
element prediction. The environmental element prediction device
(EEPD) includes a receiver 1820 for receiving the broadcast data.
In the illustrated embodiment, antenna 1822 permits receiver 1820
to receive wireless broadcasts (see, e.g., broadcast receiver 1192
of FIG. 11 receiving broadcast data from host 1150 via uplink
1190). In alternative embodiments, receiver 1820 may be coupled to
receive broadcasts using physical couplings such as wires or
optical fibers.
[0138] Device 1810 includes a memory 1840 for storing collected
data and for working memory when processor 1830 is performing the
computations required to generate point environmental element
prediction data. The device may generically be referred to as a
"weather aware device" (WAD), particularly when the environmental
elements predicted include meteorological elements.
[0139] Device 1810 includes an input/output (I/O) interface 1850
controlling external processes as well as providing an interface
between the processor 1830 and various peripherals such as a
locator 1860 or a display 1870. In one embodiment, I/O interface
1850 provides a digital output representative of an "on" or "off"
signal for control 1852. In an alternative embodiment, I/O
interface 1850 provides a proportionate signal for control 1852 in
either analog or digital form.
[0140] In one embodiment, I/O interface 1850 supports communication
of data 1856 between the device and external processes. I/O
interface 1850, for example, may support an application programming
interface (API) for retrieving data collected or computed by the
device. I/O interface 1850 may similarly provide for the receipt of
data 1856. In one embodiment, programmatic settings for the device
are received by I/O interface 1850 (i.e., data 1856). Settings may
include, for example: device region, device location, thresholds
for environmental element predictions (e.g., assume rain if
probability of precipitation exceeds 60%), etc.
[0141] In one embodiment, device 1810 includes a locator 1860 to
permit automatic determination of its location without user input.
Locator 1860, for example, may determine position of the device by
satellite telemetry. In one embodiment, locator 1860 determines the
position of the device through satellite trilateration using a
satellite constellation such as the Naystar.RTM. Global Positioning
Satellite system. A display 1870 may optionally be provided for
displaying the prediction data.
[0142] FIG. 19 illustrates an EEPD 1910 having a generalized
communication interface. Communications interface 1920 supports
receiving the data. For bi-directional communications, the
communications interface 1920 supports both transmitter and
receiver functionality (i.e., a transceiver). Bi-directional
support would be required, for example, with a client-server based
EEPD. The communications interface is coupled 1922 as appropriate
(e.g., wire, antenna, fiber optic, etc.) to communicate with the
source of the environmental element data.
[0143] Memory 1940 permits storage of collected data and provides
working memory when processor 1930 is performing the computations
required to generate point environmental element prediction data.
Device 1910 includes an input/output (I/O) interface 1950 for
controlling external processes as well as providing an interface
between the processor 1930 and peripherals such as a locator 1960
or display 1970. In one embodiment, I/O 1950 provides a digital
output representative of an "on" or "off" signal for control 1952.
In an alternative embodiment, I/O 1950 provides a proportionate
signal for control 1952 in either analog or digital form.
[0144] I/O interface 1950 may also support programmatic access to
data stored or calculated by the EEPD. I/O interface 1950 (i.e.,
data 1956), for example, may support an API for retrieving data
stored or calculated by the EEPD or alternatively for storing data
to be used by the EEPD.
[0145] In one embodiment, programmatic settings for the device are
received by I/O 1950 as data 1956. Settings may include, for
example: device region, device location, thresholds for
environmental element predictions (e.g., assume rain if probability
of precipitation exceeds 60%), etc. In one embodiment, device 1910
includes a locator 1960 to permit automatic determination of its
location without user input. Locator 1960, for example, may
determine position of the device by satellite telemetry. In one
embodiment, locator 1960 determines the position of the device
through satellite trilateration using a satellite constellation
such as the Naystar.RTM. Global Positioning Satellite system. A
display 1970 may optionally be provided for displaying the
prediction data.
[0146] EEPD 1910 may be suitable for broadcast or bi-directional
communication (e.g., client-server model) applications. This EEPD
may similarly be incorporated into media device embodiments such as
televisions, watches, radios, personal digital assistants,
electronic navigators, etc. or other broadcast reception devices as
well as devices capable of supporting bi-directional communication
(e.g., cellular telephones).
[0147] In one embodiment, the media device and the EEPD share the
same receiver (e.g., data might be broadcast on a television
channel that is otherwise unused, or alternatively broadcast during
a vertical blanking interval on one or more channels that might
otherwise be used). In an alternative embodiment, the EEPD may use
a receiver distinct from that of the media device (e.g., the EEPD
incorporates a satellite receiver or a local area network
connection distinct from the television receiver of a
television).
[0148] One device particularly suitable for implementing the EEPD
for personal use is a cellular phone. Many cellular phones already
incorporate a GPS locator to aid location in the event of an
emergency. In addition, many such phones provide a programming
environment to permit loading software applications and provide
support for accessing the Internet.
[0149] The introduction of a device capable of determining
environmental element predictions for a specific location enables
forward-looking automated control modification. Heating, air
conditioning, and irrigation systems are just a few examples of
systems that might benefit from a control system that is based at
least in part on environmental element predictions rather than
merely historical or current environmental element data.
[0150] For example, many municipalities and other legislative
bodies have required automated lawn sprinkler systems to be
outfitted with a "rain sensor" in an effort to avoid wasteful
irrigation. The rain sensor is used to inhibits or interrupt
irrigation cycles during periods of sufficient moisture.
[0151] Rain sensors utilize various techniques to measure the
moisture. The soil sensor type, for example, is inserted into the
soil and indicates when the moisture content of the earth at the
sensor exceeds a pre-determined threshold. Other cup-type sensors
capture actual rainfall and provide an indication when the amount
of water exceeds a pre-determined threshold level.
[0152] Such "rain sensors" are more accurately termed "moisture
sensors". When the input variable is moisture, the sensors indicate
current moisture conditions. The sensor output correlation with
rain is less timely.
[0153] For example, the rain sensor may not inhibit irrigation
during actual rainfall until sufficient moisture has penetrated the
ground near the rain sensor or entered the rain sensor cup.
Conversely, the rain sensor may inhibit the irrigation cycle
despite the lack of rain simply because the water in the cup or in
the ground has not evaporated. However, the rain sensor forces the
controller to be responsive to weather recently or currently
experienced (i.e., the ground has a certain moisture content
because of recent rainfall, or the water level in the cup is still
above the threshold) and may be interpreted as providing current
conditions if "moisture" is the desired control input.
Alternatively, the rain sensor may be viewed as a current control
input with a time lag or a "backwards looking" control input if the
variable being sensed is actually "rain". The term "hydrological
sensor" will be used to include rain sensors, soil sensors, and
moisture sensors collectively.
[0154] The introduction of a device that is aware of anticipated
weather conditions, permits "forward looking" control inputs for
the irrigation or other controller. Irrigation, for example, may be
inhibited or interrupted if the probability of precipitation within
a given timeframe exceeds a pre-determined threshold. Instead of a
binary "on/off" control, more sophisticated control may regulate
the timing and amount of irrigation based on the time-distributed
expected amount of precipitation.
[0155] FIG. 20 illustrates one embodiment of a controller 2010
receiving control inputs from a "forward looking" predictive
environmental element sensor 2020 and an optional hydrological
sensor 2030.
[0156] Preferably, the controller has access to both current
conditions as well as predictions for future timeframes. In the
absence of an actual current condition sensor (e.g., no
hydrological sensor) the relevant conditions may be estimated or
predicted using the predictive environmental element sensor 2020 by
using the current timeframe predictions. (Environmental element
prediction data may include predictions or estimates of current
conditions - i.e., 0 days into the future). Thus current conditions
sensors serve as auxiliary sensors. When available, the current
condition sensor will be relied upon to determine current
conditions otherwise the current conditions will be estimated using
the predictive environmental element sensor. Generally, a control
decision is made based on the "stress level" indicated by the
current environmental conditions and the probability of future
environmental conditions at that location (i.e., point
environmental element predictions) for the purpose of controlling
parameters (flow, pressure, amount) of a controlled element (water)
within a particular timeframe.
[0157] In one embodiment, the controller is an irrigation
controller that inhibits or interrupts an irrigation cycle in
accordance with a control signal from at least one of the
hydrological or the predictive environmental element sensors 2020,
2030. In an alternative embodiment, the controller varies
parameters of the controller schedule (i.e., volume of water,
length of watering time, cycle iterations, etc.) in response to
information provided by at least one of the hydrological and
predictive environmental element sensors.
[0158] If the predictive environmental element sensor is integrated
with the hydrological sensor to form an integrated sensor, existing
automated sprinkler controllers may be retrofitted to consider
current and predicted environmental element data by simply plugging
the integrated sensor into the already existing "rain sensor" input
on the irrigation controller.
[0159] FIG. 21 illustrates one embodiment of an irrigation
controller 2110 having a rain sensor input 2120. A hydrological
sensor 2132 (e.g., a rain sensor) is integrated with a predictive
environmental element sensor 2134 (e.g., an EEPD) to form an
integrated "weather switch" 2130. The hydrological sensor may sense
actual current conditions at the point location. Alternatively, the
current conditions can be estimated from the predictive
environmental element sensor.
[0160] The rain sensor input 2120 of irrigation controller is
provided with the output of the integrated weather switch 2130.
Generally the weather switch provides an output to inhibit or
interrupt irrigation if either the predictive environmental element
sensor indicates that precipitation is imminent or the hydrological
sensor indicates a period of sufficient moisture. The only time
that irrigation is not inhibited is if precipitation is not
imminent and the existing moisture level is insufficient.
"Imminence" for the predictive sensor may be determined by
likelihood of rain and the amount of rain expected. A decision tree
implemented in logic may be applied to determine whether to
irrigate, how much to irrigate, and when to irrigate within some
pre-determined timeframe.
[0161] FIG. 22 illustrates one embodiment of a process implemented
by an EEPD such as the EEPD of FIG. 19. In step 2210, the EEPD
receives environmental element prediction data. In step 2220, the
EEPD generates at least one environmental element prediction for a
point of interest. In step 2230, the EEPD optionally generates a
control signal in response to at least one of a sensed current
environmental element condition and the prediction.
[0162] More specifically to broadcast applications, FIG. 23
illustrates one embodiment of a process implemented by an EEPD
utilizing broadcast environmental element data such as the EEPD of
FIG. 18. In step 2310, the EEPD receives broadcast environmental
element prediction data. In step 2320, the EEPD generates at least
one environmental element prediction for a point of interest. In
step 2330, the EEPD optionally generates a control signal in
response to at least one of a sensed current environmental element
condition and the prediction.
[0163] The control signals of FIGS. 22 and 23 may be generated as a
result of logic applied to the predictions or sensed current
conditions. The logic may be implemented, for example, as a look-up
table, decision tree, or any other suitable device or data
structure. The control signal may simply be an "on/off" type
control. Alternatively, the control signal may provide more
sophisticated information such as when, how long, etc. to perform
an activity such as irrigation. The EEPD may generate a different
control signal for each prediction timeframe. For example, the EEPD
may interpret the predictions and/or the sensed current conditions
to provide a control signal for each prediction timeframe.
[0164] Examples of parameters that might be controlled or regulated
directly or indirectly by the control signal include electrical
power, temperature, fluid flow, etc. For irrigation applications,
the fluid is typically water. In one embodiment, the EEPD provides
the control based upon at least one of the sensed current
conditions (if available) and the predictions that the EEPD has
made about environmental elements for a point location, wherein the
predictions were derived by the EEPD from the environmental element
data it received.
[0165] Referring to FIG. 19, for example, such a control signal
might be used with respect to the predictions to select one or more
specific icons 1942 from a set of icons 1944 for visual indication
of predicted weather conditions. (Referring to FIG. 23, step 2330
need not rely on sensed current conditions when generating a
control signal or code representative of predicted weather
conditions). EEPD 1910 interprets the predictions to generate a
control signal or code for each prediction timeframe. The control
signal(s) may then be used by the EEPD or an external device to
select icons corresponding to the control signals. I/O interface
1950 is utilized to display the selected icon 1942 on display 1970
thus providing a viewer with a visual indicator corresponding to
the predictions for one or more environmental elements and one or
more prediction timeframes.
[0166] In one embodiment, the EEPD determines the appropriate icon
to display. In an alternative embodiment, an external process uses
the I/O interface 1950 to obtain data received or computed by the
EEPD (including the control signals or codes generated in response
to at least one of the sensed current conditions or the
predictions). In this latter embodiment, the control signals
generated by the EEPD may be embodied as result codes stored within
memory 1940 for communication as data 1956 when requested. The
external process then selects an icon associated with the retrieved
control signal or result code. The external process then uses the
I/O interface 1950 to display the selected icon(s). Each prediction
timeframe may have its own control signal to support iconic
representation of a plurality of prediction timeframes
simultaneously as illustrated by displayed results 1982.
[0167] Displayed results 1982 may contain textual 1986 and iconic
1984 representations of various environmental elements for the
point location. In one embodiment, data retrieved from the EEPD is
used in conjunction with other geographic information such as map
1980 to map one or more environmental variables over a geographic
region as indicated by map 1990.
[0168] The various methods described may be implemented using
processor-executable instructions provided to a processor from a
computer-readable tangible storage medium. Examples of storage
mediums suitable for storing such processor-executable instructions
include volatile storage mediums such as dynamic random access
memory as well as nonvolatile storage mediums such as read only
memories, optical disks, magnetic disks, and magnetic tape. Such a
storage medium enables distribution and deployment of the various
methods for client, server, broadcast transmitter, or broadcast
receiver implementations as the case may be.
[0169] Although sophisticated methods for predicting environmental
elements have been described, the EEPD is not limited to such
methods. For example, the EEPD may predict an environmental element
value using 1) one or more scatter points (i.e., non-grid data); 2)
one or more grid points; or 3) some combination of grid and scatter
points. The EEPD may interpolate values from any number of points
to arrive at a prediction for the point of interest. The
interpolation may be performed using linear, logarithmic, or other
weighting schemes such as those previously set forth. The EEPD may
but is not required to perform a correction to account for
prediction errors as previously set forth.
[0170] In the preceding detailed description, the invention is
described with reference to specific exemplary embodiments thereof.
Methods and apparatus for predicting environmental elements have
been described. Various modifications and changes may be made
thereto without departing from the broader spirit and scope of the
invention as set forth in the claims. The specification and
drawings are, accordingly, to be regarded in an illustrative rather
than a restrictive sense.
* * * * *
References