Operations Guide
Analysis Algorithms
- The Nowcast Engine
- Thunderstorm Identification, Tracking, and Features
- Advection
- Boundary Layer Characterization
- Boundary Layer Convergence Detection
- Variational Doppler Radar Analysis
- Satellite Algorithms
The Nowcast Engine
Cronus
Cronus gathers and combines the relevant user defined meterological data that have been generated by the various algorithms with user defined functions and weights to produce an initiation interest field and a growth and decay interest field. These These two interest fields as input into gandi.
Cronus is triggered to run by radarTrigger which watches the incoming radar beam data and sends a message or 'trigger' to cronus when a user specified tilt has begun. Cronus then sends a forecast trigger to algorithms producing data used to create the interest fields indicating that the algorithm should run and produce forecast data for specific time in future. Cronus then gathers output from the algorithms and produces its interest fields.
Gandi
Gandi takes as input the initiation and growth and decay interest fields which are ouput by cronus and the dbz advect field. User defined step functions are applied resulting in a final reflectivity or precip rate forecast. The initiation interest field is used to calculate and initiation reflectivity field (based on user defined step function.) The growth and decay interest field is used to modify the dbz advect field, either growing or decaying regions above a user specified reflectivity threshold. Finally, the initiation and growth and decay reflectivity fields are merged.
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Scripts: |
$CRONUS_HOME/bin |
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Parameters: |
$CRONUS_HOME/params |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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radarTrigger |
radar tilt indicator |
fmq |
tilt trigger |
fmq |
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cronus |
tilt trigger |
fmq |
forecast trigger |
fmq |
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user selected interest fields |
MDV |
initiation or growth and decay interest fields |
MDV |
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gandi |
advected reflectivity field |
MDV |
2D reflectivity field |
MDV |
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initiation or growth and decay interest fields |
MDV |
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Cronus operates in realtime mode by triggering at the start of the 0.5 degree radar tilt. When the nowcast domain includes several radars, the trigger can be set by MdvMerge , the application which merges the cartesian radar data from all radars.In addition to the realtime trigger, the user can manually trigger a nowcast at anytime using the graphical interface to the cronus application.
Thunderstorm Indentification, Tracking and Features
TITAN is a collection of processes which identify and track thunderstorm above a user-specified reflectivity (dBZ) threshold.
Rview and TimeHist are both graphical data displays for Titan output.
titanGrid converts titan storms characteristics into gridded fields. It can also tag storms with point statistics such as lightning strike statistics and probability of hail. Optionally, storms can be extrapolated using titan motion information.
StormInitDetect detects new storms from Titan and outputs the latitude, longitude, and time of storm initiations.
StormInit2Field grids the output of StormInitDetect and produces regions of storm initiation defined by gaussians at each storm initiation location.
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Scripts: |
$TITAN_HOME/bin |
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Parameters: |
$TITAN_HOME/params |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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Titan |
cartesian radar data |
MDV |
thunderstorm information |
Titan StormTrack |
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titanGrid |
Titan thunderstorm information |
Titan Storm Track |
2D gridded storms tagged with storm characteristics or point data statistics |
MDV |
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point data |
SPDB |
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StormInitDetect |
Titan thunderstorm information |
Titan Storm Tracks |
Points of thunderstorm initiation |
SPDB |
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StormInit2Field |
Points of thunderstorm initiation |
SPDB |
Gridded regions of thunderstorm initiation |
MDV |
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Rview |
Cartesion reflectivity data |
MDV |
Graphical data display |
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Storm information |
Titan Storm Tracks |
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TimeHist |
Storm information |
Titan Storm Tracks |
Graphical data display |
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For more information and detail on the TITAN application, see TITAN: Thunderstorm identification, tracking, analysis and nowcasting - a radar-based methodology and the TITAN User's Manual.
Advection
ctrec employs cross-correlation analysis of echos to calculate motion vectors from radar data. ('trec' is an acronym for Tracking of Radar Echos by Correlation.)
advectGrid uses ctrec motion vectors or sounding data to advect gridded data. It is usually used to advect reflectivity and storm characteristics.
startiform_filter filters flat echoes from radar data at user specified elevation. Used to isolate radar cu.
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Scripts: |
$ADVECT_HOME/bin |
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Parameters: |
$ADVECT_HOME/params |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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ctrec |
cartestian radar data |
MDV |
motion vector field |
MDV |
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advectGrid |
motion vector field from ctrec |
MDV |
2D advected dataset image |
MDV |
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sounding data used as backup to motion vector field |
SPDB |
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dataset to be advected |
MDV |
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stratiform_filter |
cartesian radar data |
2D filtered dataset |
MDV |
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For more information on ptrec see Determination of the Boundary Layer Airflow from a Single Doppler Radar.
Boundary Layer Characterization
SurfInterp is used to generate gridded surface data from surface observations.
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Scripts: |
$INGEST_HOME/bin |
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Parameters: |
$INGEST_HOME/params |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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SurfInterp |
surface observations |
SPDB |
2D field of interpolated surface data |
MDV |
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Boundary Layer Convergence Detection
Colide is a collection of algorithms designed to detect boundary features in radar data, thus providing information on surface convergence zones. The algorithms which make up Colide include:
ColLine Finds thin line interest from a radar reflectivity field.
ColShear Finds shear interest from a radar radial velocity field
ColFilt Performs a variety of actions on one or more input gridded and/or product data inputs to produce one or more gridded and/or product data outputs. Filtering actions that are supported are described in more detail under Colide filtering.
ColLineBuild creates connected line segments representing a boundary from inputs.
ColBdryTrack identifies boundaries through time, tracking their motion, from inputs and produces boundaries represented as a linked set of points with motion vectors. This is typically the final Colide output.
ColTrackForcedBdry manages human inserted boundaries and tracks their motions.
ColSpdbMerge merges product boundaries (SPDB format) from more than one source and produces a single set of output SPDB boundaries.
The following algorithms ingest and analyze colide boundary information to produce gridded data fields described below:
bdryGrid combines the linear boundaries from colide with sounding data to produce convergence zone features including boundary-relative steering flow and lifting zones. The vertical velocity field which is ouput by VDRAS is used to produce a maximum vertical velocity field associated with a boundary. The horizontal wind field output by VDRAS is used to calculate a bdry relative low level(surface -2.5 km) shear. Storm initiation location data is used to produce a field of recent initiations associated with boundaries. Boundary motion information is used to extrapolate these fields into the future.
bdryCollision extrapolates boundary position into future and maps collisions of boundaries. Sounding data is used to grow the collision in the direction of the steering level flow.
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Host: |
$COLIDE_HOST |
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Scripts: |
$COLIDE_HOME/bin |
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Parameters: |
$COLIDE_HOME/params, $COLIDE_HOME/params2 |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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ColLine |
cartesian reflectivity data (dbZ) |
MDV |
thin line interest data grid taking on values [0,1] |
MDV |
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orientation grid (angles between 0 and 90 degrees) |
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ColShear |
cartesian radial velocity data (m/s) |
MDV |
shear interest data grid taking on values [0,1] |
MDV |
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orientation grid (angles between 0 and 90 degrees) |
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ColFilt |
varied number of input grids depending on filtering option |
MDV |
varied number of output grids depending on filtering option |
MDV |
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varied non gridded data depending on filtering option |
SPDB |
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ColLineBuild |
Varied number of input grids depending on how algorithm is configured. |
MDV |
Orientation, confidence, and “distance from center” gridded fields. |
MDV |
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Varied number of sets of connected line segments, one set per algorithm configured for. |
SPDB |
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ColBdryTrack |
Varied number of input grids depending on how algorithm is configured. |
MDV |
Boundaries represented as sets of points, with motion vectors |
SPDB |
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Set of connected line segments |
SPDB |
Boundaries represented as bandaids, other gridded fields. |
MDV |
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ColTrackForcedBdry |
One triggering data source |
MDV |
One output boundary set (set of points with motion) |
SPDB |
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Human inserted boundary information |
SPDB |
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ColSpdbMerge |
Two or more input boundary sources represented as sets of points with motion. |
SPDB |
One output boundary set (sets of points with motion) |
SPDB |
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bdryGrid |
colide boundaries |
SPDB |
extrapolated 2D boundary relative steering flow field |
MDV |
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vdras output |
MDV |
extrapolated 2D lifting zones |
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storm initiation locations |
MDV |
extrapolated 2D maximum vertical velocity field associated with boundaries |
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sounding data |
SPDB |
extrapolated 2D field of recent storm initiations associated with boundaries |
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bdryCollision |
colide boundaries |
SPDB |
2D binary output for collision and lifting areas |
MDV |
Colide Filtering
Colide supports a number of filtering actions. Each instance of ColFilt can be configured to perform one or more filtering actions. A summary of the filters is as follows.
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Filter |
Description |
Input Data Description Format |
Output Data Description Format |
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COMBINE |
Combine multiple images into one |
Two or
more inputs are gridded MDV with same units. |
One output, same units/format as inputs. |
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ENHANCE |
Reduce image to leave only “long thin” clumps |
One
input, gridded MDV. |
Two
outputs, one is gridded MDV of same units as input, the other is an orientation
image (the orientation of the long/thin mask at that point). |
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ELLIPTICAL |
Filter an image for average within a
rectangle rotated to the angle giving maximum average. |
One
input, gridded MDV. |
Three outputs,
one is gridded MDV of same units as input, the other two are orientation
images (the angle rotated to). |
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THRESHOLD |
Flag data below a threshold |
One
input, gridded MDV. |
One
output, gridded MDV same units/format as input. |
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CLUMP |
Clump non-flagged data, flag data if clumps too small. Values
are unchanged within clumps. |
One
input, gridded MDV. |
One
output, gridded MDV same units/format as input. |
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SKELETON |
Reduce data to connected one pixel thick clumps. |
One
input, gridded MDV. |
One
output, gridded MDV same units/format as input. |
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SMOOTH |
Filter image for average within a rectangle. |
One
input, gridded MDV. |
One
output, gridded MDV same units/format as input. |
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DILATE |
Dilate an image (expands area where values are high). |
One
input, gridded MDV. |
One
output, gridded MDV same units/format as input. |
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MEDIAN |
Median filter applied to an image |
One
input, gridded MDV. |
One output,
gridded MDV same units/format as input. |
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ORIENTATION |
Produce orientation and confidence fields from regions and
linefits to the regions. |
Two
inputs, first is gridded MDV region data, the second is SPDB linefits to
these regions. |
Two
outputs, gridded MDV orientation and confidence. |
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BOOST |
Boost interest values near polylines. |
Two
inputs, first is gridded MDV interest data, the second is SPDB polyline data. |
One
output, gridded MDV interest data. |
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REGION |
Build regions from interest images by clumping data and
fitting expected shape/size criteria. |
One or
more MDV interest data images |
One MDV
region image. |
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REGCOMB |
Filter and combine various input regions using rules, then
rebuild the regions from what results. |
Varied
input (depends on rules) might be an MDV image of any type used as a mask,
and any number of MDV region images. |
One MDV
region image. |
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REGFILT |
Break and join regions based on history, fit and output line
segments to each such filtered region. |
One MDV
region image (to filter), one or more MDV interest data images, one MDV
region image (from tracking), one MDV orientation image, one MDV confidence
image. |
One MDV
region image. |
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RESCALE |
Scale and bias any
input data into output of the same type. |
One MDV
image. |
One MDV
image of same type as input. |
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MASK |
Flag input image where image mask image is not set |
One MDV
data image, one MDV mask image. |
One MDV
image same type as input data image. |
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MOTION_CANCEL |
Flag out areas where orient./interest are unchanging |
One MDV
data image, an optional MDV orientation image. |
One MDV
image same type as input data image. |
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HUMAN_INSERT |
Enhance values near where human enters boundary |
One MDV
data image, one SPDB human inserted boundary source. |
Four MDV
data images of same format as input, One MDV orientation image. |
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HUMAN_SUPPRES |
Suppress values near where human enters boundary |
One MDV
data image, one SPDB human inserted boundary source. |
One MDV
data image of same format as input, another MDV region image (the masked
region). |
Variational Doppler Radar Analysis (VDRAS)
VDRAS is a system that produces high-resolution three-dimensional wind in the boundary layer. The wind field is obtained through a retrieval process combining single-Doppler radar observations and a numerical model using a four-dimensional variational scheme. The output fields from VDRAS include three velocity components, divergence, and temperature.
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Scripts: |
$ADJOINT_HOME/bin |
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Parameters: |
$ADJOINT_HOME/params |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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adjoint |
PPI radar volume |
MDV |
3D wind, divergence, and temperature fields |
MDV |
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surface observations |
SPDB |
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soundings |
SPDB |
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For more information on the adjoint method see Forecasting Storm Growth and Decay using Low-level Radar Data and the Adjoint Method.
Satellite Algorithms
satDerive calculates differences and standard deviations of
satellite
channel data. Calculates short wave, infrared reflectance,
an icing index.
satThresh thresholds satellite data. Threshold values are applied to the visible and clear IR channels. Then these fields are used to mask all other fields, so that only pixels at locations that have passed the thresholding tests on IR and the visible are output - the rest are marked as bad or missing.
RateOfChange calculates the rate of change of MDV gridded data. Detects cloud growth by monitoring cloud top temperatures from infrared satellite data.
CloudClass classifies clouds based on a set of threshold based rules. Thresholds can vary sinusoidally according to the time of the day or year.
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Scripts: |
$SATELLITE_HOME/bin |
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Parameters: |
$SATELLITE_HOME/params |
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Executables |
Input Data |
Output Data |
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Description |
Format |
Description |
Format |
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satDerive |
raw satellite data |
MDV |
derived satellite data fields |
MDV |
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satThresh |
satellite data |
MDV |
thresholded satellite data |
MDV |
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RateOfChange |
IR Satellite Data |
MDV |
2D rate of change data values |
MDV |
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CloudClass |
satellite data |
MDV |
bitwise map of cloud type |
MDV |