SPACEBORNE MICROWAVE OBSERVATIONS OF RAIN 1972-1997
Thomas T. Wilheit
Texas A&M Univ.
Alfred T. C. Chang
Formerly of NASA/GSFC
Period Runs from Launch of ESMR on Nimbus 5 to Launch of TRMM Al Chang (no longer with us) was a key player in all of this. Graphics a messy problem PowerPoint didn’t exist in 1972 Color was rare. Organization of Talk IR-Based Rainfall Retrievals Passive Microwave over Ocean Passive Microwave over Land Profiling Algorithms Algorithm Workshops Radar
IR Measurements Estimates of Rainfall As far back as the ‘60s it was noted that cold clouds (IR) and bright clouds (VIS) were correlated with rainfall. Bright clouds seemed slightly better correlated but nighttime problem Thus, IR measurements attracted more interest. Geosynchronous satellites provided enough observations to be very useful. IR rain algorithms became a cottage industry. Precipitation Measurements from Space Workshop (1981) had presentations on 6 different techniques. I don’t think any are in current use.
Phil Arkin’s Method One simple method seems to have survived. Phil Arkin used the ship radar data from the 1974 GARP Atlantic Tropical Experiment (GATE) and Geosync data to examine correlations between cloud top temperature and rainfall. 6 hour accumulations had ~80% correlations with cloud top temperatures colder than 235K. (slope ~ 3mm/hr) i.e. If the cloud top is colder than 235, it’s raining 3 mm/hr. Shows a useful degree of skill. Assorted refinements over the years The point is the desperation of the meteorological community for rainfall data.
Microwave Radiometry Comes to the Rescue Launch of Nimbus 5 December 1972 Electrically Scanned Microwave Radiometer: 19.35 GHz, 25km @nadir ±50° cross-track scan (45 x 165 km resolution @ edges), H-pol
ESMR Calibration Accuracy ca. 5K (@Nadir) NE Δ T ca. 2K Single Frequency so Geophysical Noise was Worse. Initial Images Had Terrible Streaks. Traced to Cross Polarized Grating Lobes. Transformed so that All Beam Positions Statistically Like Nadir BP Coastlines and Ice Edge Blurred in Mosaics Predictive Ephemerides were Lousy. Deployment Mechanism Made the Antenna Rock 6° p-p After all this was cleaned up we could do some science.
Typical Quick Look Image from ESMR Land Features Obvious Features over the Ocean with TBs too high to be explained by SST, Wind, Non-Raining Cloud
Quantitative Theory OK, we can see rain (of some unspecified intensity) over the ocean. Can we be more quantitative? Ed Rodgers and Merle Rao compared ESMR data with WSR-57 radar data from Miami. It looked reasonably good, but we needed a theory. Equations for radiative transfer in rain are messy but well-known. But how to solve them??? To get the radiance in any one direction, we need the radiance scattered in from all other directions. Bob Curran had brought a program originally written by Ben Herman (U. Arizona) to GSFC. He gave Al Chang a copy and Al converted it for microwave. Now what do we put into the equation of radiative transfer?
19.35 GHz Ground-Based 37 GHz Ground-Based ESMR vs Miami WSR-57
Early Applications Merle Rao, Bill Abbott and John Theon collaborated to generate an atlas of oceanic rainfall from ESMR Quality control problems—mostly from ephemerides. Freezing level problem First observation of the South Atlantic Convergence Zone? Bob Adler and Ed Rodgers looked at the energy balance of a hurricane Results were reasonable Beam Filling was ignored in all these applications
SSM/I First SSM/I was launched on DMSP F-8 in 1987 19.35 22.235, 37 & 85.5 GHz Dual Pol except @ 22. (85V failed early on F-8) 6 Subsequent Copies Additional Channels and Better Calibration Better Rainfall Retrievals. Rain Algorithm Developed for Global Precipitation Climatology Project 5° x 5° x 1 Month Boxes Freezing Level from 19V /22V combination Rain from Histograms of 2*TB19V – TB22V Linear Combination Mitigated Water Vapor Variability Fit Parameters of Mixed Log-Normal Rain PDF to TB histogram Chiu’s Beam Filling Correction
SSM/I Derived Rainfall Amount August 1987
What about rain over land? High & Variable emissivity makes it difficult This is the late-’70s view. Basis of 85.5 GHz channel on SSM/I
Nimbus-6 ESMR 37 GHz, Conical Scan, Dual Pol Ed Rodgers and Honnappah Siddalingaiah looked at ESMR-6 over land
1978 Tropical Storm Cora Flight Don’t try this kind of logic at home; I’m a paid professional SSM/I land rain capability based on liquid hydrometeor scattering. Observed at 37 GHz/ Should be better at higher frequencies Ga. Tech had a 91.65 GHz radiometer suitable for flight on the NASA CV 990 CV-990 cannot fly over/through interesting land rain (too rough) It can fly through most oceanic precipitation At these frequencies interesting rain (10s of mm/h) are opaque. Land surface emissivity doesn’t matter. So we flew over ocean to test a land rain capability Expected to see Tbs of 240 to 250K with little polarization
Rain over land can be seen via scattering by ice. Bergeron Rain Drop Formation Process Variability in size distribution/ layer thickness makes a quantitative relationship difficult.
Profiling Algorithms After Launch of SSM/I Two Groups: Kummerow and Smith Interesting Problem Attracted Many New Researchers into Rain Two Obvious Pieces of Information in Oceanic Radiances Attenuation of Liquid Layer Scattering by Frozen Layer Additional Degrees of Freedom More Subtle Kummerow Moved to Bayesian Approaches with Additional Information from Database
Algorithm Intercomparison Projects NASA/WETNET PIP PIP-1 Aug-Nov 1987 Global PIP-2 1987-1993 17S to 60N (27 cases) PIP-3 1992 Global + Jan. & Jul. 1991 &1993 Global Precipitation Climatology Project Algorithm Intercomparison Project AIP-1 Summer 1987 Japan AIP-2 Winter/Spring 1991 Europe AIP-3 Austral Summer ‘92-’93 TOGA/COARE IR and Microwave Algorithms, Physical and Empirical Ground Truth Difficult to Impossible IR algorithms No Physics but Lots of Samples Microwave Scattering Weak Physics and Very Poor Sampling Microwave Absorption Good Physics and Very Poor Sampling (Ocean Only) Performance Depends on How a Given Scenario Relates to Strengths & Weaknesses above
Why not fly a Radar? Suggested as early as the ‘50s (Harry Wexler) If you think of a Radar in isolation One you can afford is pretty much useless A useful one costs the gross national product. Any reasonable Radar will have a very narrow swath. No Sampling Think of a Radar as part of a rain measurement system. Radar is a physics probe a calibrator.