# Presentation: 000322

## Remote Sensing in Hydrology

- Robert J. Kuligowski, Ph. D.
- NOAA/NESDIS Office of Research and Applications
- Bob.Kuligowski@noaa.gov
- Presentation to NWS/WMO Hydrologic Forecasting Course
- Kansas City, MO
- 29 October 2003

## Outline

- Precipitation
- Theory/basis—visible/IR and microwave
- Specific Algorithms and Examples
- Forecasting Tools
- Validation Efforts
- Surface Conditions
- Vegetation
- Snow
- Surface Wetness
- Flood Inundation

## Part I:  Precipitation

**Theory/basis—visible/IR and microwave**
- Specific Algorithms and Examples
- Forecasting Tools
- Validation Efforts

## Precipitation—Theory/Basis 

**Visible (VIS)/Infrared (IR) Algorithms**
- Basic assumption is that cloud-top temperature brightness is related to cloud height, which in turn is related to cloud thickness, and to rainfall rate
- Colder, brighter clouds are associated with heavier rain
- Warmer, less bright clouds are associated with light or no rain
- Reasonable assumption for convective clouds
- Poor assumption for
- stratiform clouds (warm, but wet)
- cirrus clouds (cold, but rain-free)

## Cirrus

- Illustration of the IR signal from different cloud types

**T****b****=210 K**

- Nimbostratus
**T****b****=240 K**

**200**

**250**

**290**

**T (K)**

- Cumulonimbus
**T****b****=200 K**

## Precipitation—Theory/Basis 

**Microwave (MW) Algorithms**
- _Scattering_:  ice in clouds scatters (warm) terrestrial radiation downward, producing cold areas in imagery.
- Rainfall rates are related to the magnitude of the resulting brightness temperature depression.
**Strength**:  can be applied to high-frequency channels where surface effects are not detected:  works over both land and ocean
**Weakness**:  poor at detecting precipitation clouds with little or no ice (e.g. warm orographic clouds in the tropics)

## Precipitation—Theory/Basis 

**Microwave (MW) Algorithms**
- _Emission_:  water in clouds emits radiation, can be seen against a radiatively cold background (i.e. oceans).
- Rainfall rates are related to the magnitude of the resulting brightness temperature difference
**Strength**:  Sensitive to clouds with little or no ice
**Weakness**:  must know terrestrial radiances without cloud beforehand; generally applicable over oceans but not land

## Land (Scattering)

**Land (Scattering)**

**Ocean (Emission)**

**Lower ****T****b ****above cloud**

**Higher ****T****b ****above clear air**

**Lower ****T****b ****above clear air**

**Higher ****T****b ****above cloud**

- Low ε

- High ε

## Precipitation—IR/VIS vs. MW 

**Physical Robustness:**
- Microwave radiances are sensitive to moisture throughout the cloud
- IR/VIS data reflect cloud-top conditions only and thus are more weakly related to actual rainfall rates over a wider range of conditions than MW radiances.
**Space/Time Resolution**
- IR/VIS data are available at 4 km/1km resolution (GOES) on geostationary platforms, allowing looks in many locations every 15 minutes—suitable for extreme precipitation events at short time scales
- MW instruments are presently restricted to polar-orbiting platforms, limiting views to 2 per day per satellite—more suitable for larger scales in time and space

## Part I:  Precipitation

- Theory/basis—visible/IR and microwave
**Specific Algorithms and Examples**
**IR Algorithms**
**Microwave Algorithms**
**Blended Algorithms**
- Forecasting Tools
- Validation Efforts

## IR Algorithms:  Hydro-Estimator (H-E)

- The operational satellite rainfall algorithm at NESDIS since August 2002
- Uses 10.7-μm (T10.7) brightness temperature to determine raining areas and rain rates
- Assigns rain only to regions where T10.7 is below local average; i.e. active precipitating cores
- Rain rates are a function of both T10.7 and its value relative to the local average—further enhancement of rain rates in precipitating cores

## H-E Continued

**Sample Hydro-Estimator Rain Rate Curves as a Function of Precipitable Water**

## H-E Continued

- Adjustments using Numerical Weather Prediction Model Data, including:
- Relative humidity to reduce precipitation in arid regions with significant sub-cloud evaporation of raindrops
- Wind fields interfaced with digital topography to determine orographic enhancements or reductions of precipitation
- Also adjustments for parallax in regions away from satellite sub-point

## H-E Availability

- Produced every 15 minutes over the continental United States using GOES-10 and -12 data
- Produced in other regions throughout the globe whenever IR imagery are available
- Instantaneous rates are summed into 1-, 3-, 6- and 24-hourly totals
- Available in real-time for entire Western Hemisphere over the Internet from the NESDIS Flash Flood Home page
- Contact:  Rod Scofield at Roderick.Scofield@noaa.gov

## H-E—Where to Get Data

- http://orbit-net.nesdis.noaa.gov/arad/ht/ff

**Continental U.S**.

**Central & South America**.

## H-E—Example

**H-E—Example**

**24-h Total Ending 1200 UTC 7 October 2003**

**T.D. Nora**

**T.D. Olaf**

## IR Algorithms:  GMSRA

- GMSRA=**G**OES **M**ulti-**S**pectral **R**ainfall **A**lgorithm
- Uses Data from 4 Different Channels:
- Visible (0.69 μm)—discriminate between thin (nonraining) cirrus and thicker (raining) clouds
- “Short” IR Window (3.9-μm)—use reflectivity to identify clouds that are warm but have large particles near cloud-top and are thus producing rain
- Water Vapor (6.7-μm)—warm signature above overshooting cloud tops differentiates from cirrus
- IR Window (10.7-μm)—texture screening of cirrus clouds (low texture=cirrus; high texture=rain) and calculation of rainfall rate (but dependent only on value at pixel of interest)

## GMSRA Continued

- Adjustment for rainfall evaporation using precipitable water and relative humidity data from numerical weather models
- Produced every 15 minutes for the continental United States using GOES-10 and -12 data
- Instantaneous rates are summed into 1-, 3-, 6- and 24-hourly totals
- Available in real-time over the Internet from the NESDIS Flash Flood Home page
- Contact:  Mamoudou Ba at mba@atmos.umd.edu

## GMSRA—Where to Get Data

- http://orbit-net.nesdis.noaa.gov/arad/ht/ff

## GMSRA—Example

**24-h Total Ending 1200 UTC 6 October 2003**

## MW Algorithms:  SSM/I

- SSM/I=**S**pecial **S**ensor **M**icrowave/**I**mager
- Seven channels:  19, 22, 37, and 85 GHz with both horizontal (H) and vertical (V) polarization (except no H on 22 GHz)
- Two separate algorithms:
- Emission: TB at 19V, 22V used to create “clear-sky” 37V values—degree of warming of observed 37V related to rainfall rate _over water_ in regions of _weak scattering_
- Scattering:  TB at 19V, 22V used to estimate “clear-sky” 85V values—degree of cooling of observed 85V related to rainfall rate (separate calibrations for land and ocean)

## SSM/I Continued

- Approximate horizontal resolution of 25 km
- Maximum rainfall rate of 35 mm/h
- Available 6 times per day (~0600, 0915, 1100, 1800, 2115, 2300 Local Standard Time); estimates produced globally as data become available
- Contact:  Ralph Ferraro at Ralph.R.Ferraro@noaa.gov

## SSM/I—Where to Get Data

- http://www.osdpd.noaa.gov/PSB/SHARED_PROCESSING/SHARED_PROCESSING.html

## SSM/I—Example

**Rain Rate During the 4 h Ending 1600 UTC 8 October 2003**

## MW Algorithms—AMSU-B

- AMSU=**A**dvanced **M**icrowave **S**ounding **U**nit
- Single scattering algorithm over both land and ocean:  difference between brightness temperatures at 89 and 150 GHz is related to rainfall rate
- Maximum rain rate of 35 mm/h
- Approximately 16-km horizontal resolution
- Available 6 times per day (~0130, 0730, 1030, 1330, 1930, and 2230 Local Standard Time); estimates produced globally as data become available
- Contact:  Ralph Ferraro at Ralph.R.Ferraro@noaa.gov

## AMSU-B—Where to Get Data

- http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/index.html

## AMSU-B—Example

**NOAA-16 Rain Rate at 1330 LST 7 October 2003**

## IR-Microwave Algorithms

- Numerous algorithms for combining the relative accuracy of MW rainfall rates with the space/ time resolution of IR data
- Most algorithms calibrate a relationship between MW rainfall rates and 10.7μm IR brightness temperatures
- Three types (Turk algorithm, PERSIANN, and CMORPH) running in real-time at NOAA

## IR-MW Algorithms:  Turk

- Developed by F. J. Turk of Naval Research Laboratory (NRL)
- Determines relationship between MW rain rate and IR brightness temperature by matching cumulative distribution functions (CDF’s); e.g. 90th percentile rain rate is assigned to temperature corresponding to 90th percentile
- Calibration updated every few hours for a 5x5-degree region
- Also includes H-E adjustments based on model data (moisture, etc.)
- Contact:  Joe Turk at turk@nrlmry.navy.mil

## Turk Blend—Where to Get Data

-  or (CONUS only): _[http://orbit-](http://orbit-net.nesdis.noaa.gov/arad/ht/ff/blended.html)__[net.nesdis.noaa.gov/arad/ht/ff/blended.html](http://orbit-net.nesdis.noaa.gov/arad/ht/ff/blended.html)_

- http://www.nrlmry.navy.mil/sat-bin/rain.cgi

## Turk Blend—Example

**24-hour Total Ending 0000 UTC 9 October 2003**

## IR-MW Algorithms:  PERSIANN

**P**recipitation **E**stimation from **R**emotely **S**ensed **I**nformation using **A**rtificial **N**eural **N**etworks
- Developed by Soroosh Sorooshian’s research group at the U. of Arizona (now at UC-Irvine)
- Uses artificial neural networks to relate 10.7-μm brightness temperature (both pixel values and local patterns) to MW rainfall rates
- About to begin routine global estimates at 0.25-degree resolution
- Will have link from NESDIS Flash Flood Web page
- Contact:  Bob Kuligowski at Bob.Kuligowski@noaa.gov

## PERSIANN—Example

**6-hour Total Ending 1200 UTC 15 July 2003**
**(Tropical Storm Bill)**

**PERSIANN**

**Radar-Raingauge Blend**

## IR-MW Algorithms:  CMORPH

- CMORPH=**C**PC **MORPH**ing technique
- Developed by R. Joyce and colleagues at the Climate Prediction Center (CPC)
- Uses IR imagery to interpolate the movement of rainfall areas in MW imagery in between images
- Also interpolates growth/decay of MW rainfall between MW images
- Produced globally at 0.727-degree resolution in near-real time
- Contact:  Bob Joyce at Robert.Joyce@noaa.gov

## CMORPH—Example

**CMORPH—Example**

**24-h Totals ending 0000 UTC 8 October 2003**

## CMORPH—Where to Get the Data

- http://www.cpc.noaa.gov/products/janowiak/MW-precip_index.html

## Part I:  Precipitation

- Theory/basis—visible/IR and microwave
- Specific Algorithms and Examples
**Forecasting Tools**
**TRaP**
**Hydro-Nowcaster**
- Validation Efforts

## Forecasting Tools:  TRaP

- TRaP=**T**ropical **Ra**infall **P**otential—24-hour precipitation forecast
- Produced by extrapolating microwave-based instantaneous precipitation estimates along the _predicted_ storm track
- Forecasts produced automatically whenever a new microwave image or track forecast becomes available—posted in Web in graphic format

## TRaP—Where to Get the Data

- http://www.ssd.noaa.gov/PS/TROP/trap-img.html

## TRaP—Example(Hurricane Claudette)

**Pre-landfall TRaP for the 24 h ending 1200 UTC 16 July 2003**

**TRaP Forecast**

**Radar-Raingauge Blend**

**TRaP—Example****(Hurricane Claudette)**

## TRaP—Example

**TRaP for Tropical Cyclone Manou—1815 UTC 8 May 2003**

## Forecast Tools:  Hydro-Nowcaster

- 0-3 hour nowcasts of precipitation amount
- Based on extrapolation of H-E instantaneous rainfall rates along the direction of _movement_ of precipitation cells
- Rain rates are enhanced or reduced with time based on previous trends
- Produced routinely for the continental U.S.

## Hydro-Nowcaster—Where to Get Data

**Hydro-Nowcaster—Where to Get Data**

- http://orbit-net.nesdis.noaa.gov/arad/ht/ff

## Hydro-Nowcaster—Example

**Hydro-Nowcaster—Example**

**3-h Forecast for 1345-1645 UTC 9 October 2003**

## Part I:  Precipitation

- Theory/basis—visible/IR and microwave
- Specific Algorithms and Examples
- Forecasting Tools
**Validation Efforts**

## Satellite QPE Validation:  NESDIS

- Validation over continental U.S.  against raingauges (24-h amounts) and 4-km Stage IV radar/raingauge blend (6-h amounts)
- Statistics produced and displayed once per day
- Daily spatial plots of all algorithms for 6 regions for comparison
- Archive of digital data also available for analysis

## NESDIS Satellite QPE Validation Web Page

- http://orbit-net.nesdis.noaa.gov/arad/ht/ff/validation/validation.html

**Digital**
**Archive**

**Click on map**
**regions for**
**statistics**

**Algorithm**
**descriptions**

## Satellite QPE Validation:  CPC

- Validation against CPC daily 0.25-degree raingauge analysis over the continental U.S.
- Evaluation of 14 different IR, MW, and PR+MW algorithms from different agencies
- Numerous statistics for comparison, plus spatial plots of all algorithms

## CPC Validation Web Page

- http://cpc.ncep.noaa.gov/products/janowiak/us_web.html

## Satellite QPE Validation:  Australia

- Validation over Australia against 0.25-degree daily raingauge analysis
- Evaluation of 13 different IR, MW, and PR+MW algorithms plus precipitation forecasts from 4 numerical weather models
- Numerous statistics for comparison, plus spatial plots of all algorithms

## Australian Bureau of Meteorology Validation Page

- http://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval_dev.html

## Part II:  Surface Conditions

**Vegetation**
- Snow
- Surface Wetness
- Flood Inundation

## Vegetation Products

**http://www.osdpd.noaa.gov/PSB/IMAGES/gvi.html**

## Vegetation Products:  NDVI

**N**ormalized **D**ifference **V**egetation **I**ndex
- Values from -0.1 to +0.703
- NDVI<0—bodies of water, clouds, rain, or snow
- 0<NDVI<0.1—rocks, bare soil, desert
- NDVI>0.1—vegetation, with biomass proportional to NDVI value

- 0.0

- 0.1

## Vegetation Products:  Fractional Vegetation

- NDVI converted to fractional vegetation using a linear relationship:
- NDVI≤0.070.0 fraction
- NDVI≥0.571.0 fraction

## Vegetation Products:  Vegetative Health

- Uses a combination of visible, 0.85-μm, and 10.8-μm bands of the polar-orbiting Advanced Very High Resolution Radiometer (AVHRR)
- Sensitive to clorophyll and moisture content in the vegetation and to surface temperature
- Ranges from 0 (extremely poor) to 50 (fair) to 100 (excellent)
- Values below 35 indicate vegetative stress and drought
- 16-km spatial resolution; updated once per week
- Contact:  Felix Kogan at Felix.Kogan@noaa.gov

## Vegetative Health:  Where to Get the Data

- http://orbit-net.nesdis.noaa.gov/crad/sat/surf/vci/index.html

## Vegetative Health:  Example

- Comparison for Africa for 05 October 2002 and 2003

## Part II:  Surface Conditions

- Vegetation
**Snow**
**Automated**
**Manual**
- Surface Wetness
- Flood Inundation

## Automated Snow Products—Where to Get Data

- http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/index.html

**Link to current products**

**Link to previous day’s products**

**Experimental Snow Water Equivalent (SWE)**

## Automated Snow Cover—Example

**NOAA-16 AMSU Snow Cover at 1330 LST 9 October 2003**

## Snow Water Equivalent (SWE)—Example

**NOAA-16 AMSU SWE for 9 October 2003**

## Manual Snow Products—Where to Get Data

**Manual Snow Products—Where to Get Data**

**http://www.ssd.noaa.gov/PS/SNOW/**

**Data archive from past years**

**Current and recent daily snow products by region**

## Manual Snow Cover—Example

**Snow Cover for 9 October 2003**

- 	Estimates are produced daily by Satellite Analysis Branch (SAB) forecasters using satellite imagery and surface observations.

## Part II:  Surface Conditions

- Vegetation
- Snow
**Surface Wetness**
- Flood Inundation

## Surface Wetness—Where to Get Data

- http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/index.html

**Link to experimental surface wetness for US and China**

## Surface Wetness—Example

**NOAA-16 AMSU Wetness Products for 9 October 2003**

**Surface Wetness and Precipitation**

**24-h Change in Surface Wetness**

## Part II:  Surface Conditions

- Vegetation
- Snow
- Surface Wetness
**Flood Inundation**

## Flood Inundation

- http://www.dartmouth.edu/~floods

- Monitors flooding worldwide
- Uses data from a combination of satellites to produce inundation images for larger floods
- Archives available

## Flood Inundation—Example

**Flooding in southwestern Mexico from Hurricane Marty—24 September 2003**

## Questions?