Anthony DeAngelis

Abstract Estimation of precipitation provides useful climatological data for researchers; as well as invaluable guidance for forecasters of flash floods, widespread heavy rain, and heavy snow. Satellites present one of the many tools used to provide spatial representations of precipitation rates and totals from past events. Satellite based precipitation measurements are needed because ground based radar and rain gauges have spatial and technical limitations. Satellite precipitation estimates are derived from common satellite parameters, such as GOES 11 and 12 cloud-top temperatures, and brightness temperature at various wavelengths from several microwave satellites- to name a few. The infrared theory relies on the relationship between cloud-top temperature and precipitation rate, while the microwave theory depends on the relationship between ice and water content in clouds and precipitation. By combining both theories into one algorithm, the future of space based precipitation estimation appears promising.

Outline Why use satellites to estimate precipitation? Why use satellites to estimate precipitation? Satellites used in precipitation estimation Satellites used in precipitation estimation GOES Auto-Estimator Algorithm GOES Auto-Estimator Algorithm Applications Applications

Outline Why use satellites to estimate precipitation? Why use satellites to estimate precipitation? Satellites used in precipitation estimation Satellites used in precipitation estimation GOES Auto-Estimator Algorithm GOES Auto-Estimator Algorithm Applications Applications

Ways to Estimate Precipitation Ground based radar (ie. NEXRAD) Ground based radar (ie. NEXRAD) Network of Rain Gauges Network of Rain Gauges Satellites Satellites

Why Use Satellites? Significantly expands spatial coverage- within US, outside US, and over oceans Significantly expands spatial coverage- within US, outside US, and over oceans

US NEXRAD “Gaps” (West)

Why Use Satellites? Significantly expands spatial coverage- within US, outside US, and over oceans Significantly expands spatial coverage- within US, outside US, and over oceans No beam block (mountains, buildings, insects) No beam block (mountains, buildings, insects)

NEXRAD Beam Block (West)

Why Use Satellites? Significantly expands spatial coverage- within US, outside US, and over oceans Significantly expands spatial coverage- within US, outside US, and over oceans No beam block (mountains, buildings, insects) No beam block (mountains, buildings, insects) No signal attenuation effects No signal attenuation effects

Ground Based Radar Signal Attenuation

Why Use Satellites? Significantly expands spatial coverage- within US, outside US, and over oceans Significantly expands spatial coverage- within US, outside US, and over oceans No beam block (mountains, buildings, insects) No beam block (mountains, buildings, insects) No signal attenuation effects No signal attenuation effects Less need for calibration- often using only one instrument (such as GOES) Less need for calibration- often using only one instrument (such as GOES)

Why Use Satellites? Significantly expands spatial coverage- within US, outside US, and over oceans Significantly expands spatial coverage- within US, outside US, and over oceans No beam block (mountains, buildings, insects) No beam block (mountains, buildings, insects) No signal attenuation effects No signal attenuation effects Less need for calibration- often using only one instrument (such as GOES) Less need for calibration- often using only one instrument (such as GOES) Supplements ground based radar and rain gauge estimates Supplements ground based radar and rain gauge estimates

Outline Why use satellites to estimate precipitation? Why use satellites to estimate precipitation? Satellites used in precipitation estimation Satellites used in precipitation estimation GOES Auto-Estimator Algorithm GOES Auto-Estimator Algorithm Applications Applications

Main Satellites Used Infrared Infrared GOES 11 and 12 GOES 11 and 12 Microwave Microwave DMSP SSM/I DMSP SSM/I NOAA AMSU-A NOAA AMSU-A NOAA AMSU-B NOAA AMSU-B NASA AMSR-E NASA AMSR-E Satellite Radar Satellite Radar NASA TRMM NASA TRMM

Infrared Theory Relationship between cloud-top temperature, cloud-top thickness, and precipitation: Relationship between cloud-top temperature, cloud-top thickness, and precipitation: Colder cloud-top temperatures imply higher and thicker clouds, which imply heavier precipitation Colder cloud-top temperatures imply higher and thicker clouds, which imply heavier precipitation Relationship between changes in cloud-top surface and precipitation Relationship between changes in cloud-top surface and precipitation Vertically growing clouds are associated with precipitation while decaying clouds are not Vertically growing clouds are associated with precipitation while decaying clouds are not

Microwave Theory Direct relationship between ice in cold clouds and precipitation Direct relationship between ice in cold clouds and precipitation Ice scatters terrestrial radiation back down to the surface, making microwave imagery appear “cold” where ice clouds are present (passive) Ice scatters terrestrial radiation back down to the surface, making microwave imagery appear “cold” where ice clouds are present (passive) Direct relationship between water content in clouds and precipitation Direct relationship between water content in clouds and precipitation Water in clouds emits microwave radiation, making microwave imagery relatively “warm” where high water content clouds are present (passive) Water in clouds emits microwave radiation, making microwave imagery relatively “warm” where high water content clouds are present (passive)

Outline Why use satellites to estimate precipitation? Why use satellites to estimate precipitation? Satellites used in precipitation estimation Satellites used in precipitation estimation GOES Auto-Estimator Algorithm GOES Auto-Estimator Algorithm Applications Applications

GOES Review Geostationary- stays over the same place 24/7, semi-major altitude of ~ 42,000km Geostationary- stays over the same place 24/7, semi-major altitude of ~ 42,000km GOES 11- W. Hem., GOES 12- E. Hem. GOES 11- W. Hem., GOES 12- E. Hem.

History of GOES Algorithms Interactive Flash Flood Analyzer (IFFA) Interactive Flash Flood Analyzer (IFFA) Manual and time consuming Manual and time consuming Auto-Estimator (AE) (GOES 11/12) Auto-Estimator (AE) (GOES 11/12) Similar to IFFA but automated and more advanced Similar to IFFA but automated and more advanced Hydro-Estimator (HE) (GOES 11/12) Hydro-Estimator (HE) (GOES 11/12) Similar AE but more advanced and operational Similar AE but more advanced and operational GOES Multi-Spectral Rainfall Algorithm (GMSRA) (GOES 11/12) GOES Multi-Spectral Rainfall Algorithm (GMSRA) (GOES 11/12) Uses all 5 GOES imaging channels Uses all 5 GOES imaging channels

History of GOES Algorithms Interactive Flash Flood Analyzer (IFFA) Interactive Flash Flood Analyzer (IFFA) Manual and time consuming Manual and time consuming Auto-Estimator (AE) (GOES 11/12) Auto-Estimator (AE) (GOES 11/12) Similar to IFFA but automated and more advanced Similar to IFFA but automated and more advanced Hydro-Estimator (HE) (GOES 11/12) Hydro-Estimator (HE) (GOES 11/12) Similar AE but more advanced and operational Similar AE but more advanced and operational GOES Multi-Spectral Rainfall Algorithm (GMSRA) (GOES 11/12) GOES Multi-Spectral Rainfall Algorithm (GMSRA) (GOES 11/12) Uses all 5 GOES imaging channels Uses all 5 GOES imaging channels

GOES Auto-Estimator Procedure Step 1: Measure cloud-top brightness temperature. Initiate rainfall rate based on a non-linear (power-law) relationship between cloud-top temperature and rainfall rate produced by ground based radar Step 1: Measure cloud-top brightness temperature. Initiate rainfall rate based on a non-linear (power-law) relationship between cloud-top temperature and rainfall rate produced by ground based radar Uses 10.7μm channel Uses 10.7μm channel

Nonlinear Power-Law Relationship R = *10 11 * exp ( * T 1.2 ) R in mm/hour, T in °K, Developed in 1970s

GOES Auto-Estimator Procedure Step 2: Account for the availability of atmospheric moisture- where rainfall rate obtained from the first step is multiplied by a correction factor Step 2: Account for the availability of atmospheric moisture- where rainfall rate obtained from the first step is multiplied by a correction factor Uses NCEP Eta model relative humidity (RH) and precipitable water (PW) Uses NCEP Eta model relative humidity (RH) and precipitable water (PW)

Accounting for Atmospheric Moisture Determine a moisture correction factor PWRH: Determine a moisture correction factor PWRH: PWRH = PW (sfc. to 500mb) * RH (sfc. to 500mb) PWRH = PW (sfc. to 500mb) * RH (sfc. to 500mb) Scale PWRH empirically between 0.00 and 2.00 Scale PWRH empirically between 0.00 and 2.00 Multiply precipitation rate by PWRH Multiply precipitation rate by PWRH If T B < 210K, do not multiply by PWRH (implies enough moisture) If T B < 210K, do not multiply by PWRH (implies enough moisture) If T B < 200K, rainfall rate limited to 72mm/hr (maximum average rainfall rate in US) If T B < 200K, rainfall rate limited to 72mm/hr (maximum average rainfall rate in US)

GOES Auto-Estimator Procedure Step 3: Determine where rain is actually falling -assume rain to be falling only where there are growing clouds exhibiting over-shooting tops Step 3: Determine where rain is actually falling -assume rain to be falling only where there are growing clouds exhibiting over-shooting tops This uses consecutive half-hourly IR images This uses consecutive half-hourly IR images

Screening Out Non-Raining Pixels Use consecutive images to see where cloud-tops are becoming colder (growing) or becoming warmer (decaying) Use consecutive images to see where cloud-tops are becoming colder (growing) or becoming warmer (decaying) Establish cloud growth correction factor (CG) Establish cloud growth correction factor (CG) CG= 1, where cloud-tops are growing CG= 1, where cloud-tops are growing CG=0, where cloud-tops are decaying CG=0, where cloud-tops are decaying Multiply precipitation rate by CG, acts like “rain mask” Multiply precipitation rate by CG, acts like “rain mask”

GOES Auto-Estimator Summary Rainfall rate= power-law rainfall rate (R) * moisture correction factor (PWRH) * cloud growth correction factor (CG) Rainfall rate= power-law rainfall rate (R) * moisture correction factor (PWRH) * cloud growth correction factor (CG) Products: Products: 15 minute rainfall rates 15 minute rainfall rates 1 hr, 3hr, 6hr, 24hr totals 1 hr, 3hr, 6hr, 24hr totals Mainly US Mainly US Mainly used for heavy convective precipitation Mainly used for heavy convective precipitation Hydro-estimator- few extra steps Hydro-estimator- few extra steps

Example 24-hr Total

GOES Precipitation Overview Advantages: Advantages: High temporal resolution (geostationary)- precipitation estimates every 15 minutes High temporal resolution (geostationary)- precipitation estimates every 15 minutes Fairly high spatial resolution of about 4km Fairly high spatial resolution of about 4km Disadvantages: Disadvantages: Crude scientific theory which doesn’t always hold Crude scientific theory which doesn’t always hold Can mistake cumulonimbus for cold cirrus Can mistake cumulonimbus for cold cirrus Weaker performance for non-convective precipitation Weaker performance for non-convective precipitation

Outline Why use satellites to estimate precipitation? Why use satellites to estimate precipitation? Satellites used in precipitation estimation Satellites used in precipitation estimation GOES Auto-Estimator Algorithm GOES Auto-Estimator Algorithm Applications Applications

Applications Forecasting of heavy precipitation Forecasting of heavy precipitation Use NOAA AMSU-B (microwave) hourly rainfall rates as input Use NOAA AMSU-B (microwave) hourly rainfall rates as input Uses T B at 89 and 150GHz regressed against radar data over both land and ocean (scattering theory- passive) Uses T B at 89 and 150GHz regressed against radar data over both land and ocean (scattering theory- passive) Temporal resolution: 4 times a day Temporal resolution: 4 times a day Spatial resolution: ~ 16km Spatial resolution: ~ 16km Use Tropical Rainfall Potential (TRaP) algorithm to produce a 24 hour precipitation forecast Use Tropical Rainfall Potential (TRaP) algorithm to produce a 24 hour precipitation forecast

Satellite Precipitation Forecasting Overview Advantages: Does a fairly good job forecasting the maximum amount of rainfall expected Advantages: Does a fairly good job forecasting the maximum amount of rainfall expected Disadvantages: Does a poor job forecasting the spatial extent of heavy rainfall Disadvantages: Does a poor job forecasting the spatial extent of heavy rainfall

12UTC Jun 5 to 12UTC Jun h ending 12 UTC Jun ” max TRaP CalculationsETA Model Forecast Stage III multi-sensor observations Tropical Storm Allison 3” 12.5” max

Conclusions Satellites are superior to alternative methods with respect to coverage and calibration. Satellites are superior to alternative methods with respect to coverage and calibration. Satellites supplement ground radar and rain gauges- they are all interconnected. Satellites supplement ground radar and rain gauges- they are all interconnected. GOES products are superior to microwave due to higher temporal and spatial resolution. GOES products are superior to microwave due to higher temporal and spatial resolution. Microwave products have more robust scientific theory than GOES. Microwave products have more robust scientific theory than GOES. A combination of microwave and infrared theory is promising for the future. A combination of microwave and infrared theory is promising for the future.

Main References Vicente G.A., R.A. Scofield, and W.P. Menzel, 1998: The Operational GOES Infrared Rainfall Estimation technique. Bulletin of the American Meteorological Society, 79, Vicente G.A., R.A. Scofield, and W.P. Menzel, 1998: The Operational GOES Infrared Rainfall Estimation technique. Bulletin of the American Meteorological Society, 79, /docs/Kuligowski/hydromet02.ppt /docs/Kuligowski/hydromet02.ppt tml tml