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Remote Sensing of Precipitation

Remote Sensing of Precipitation. World’s Disaster Statistics. (cpc.ncep.noaa.gov). (cpc.ncep.noaa.gov). (www.roc.noaa.gov). There are three major ways to measure precipitation: rain gauges, ground radars and satellites. Other possible ways: Cell phone network signals (Messer, 2007).

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Remote Sensing of Precipitation

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  1. Remote Sensing of Precipitation

  2. World’s Disaster Statistics

  3. (cpc.ncep.noaa.gov) (cpc.ncep.noaa.gov) (www.roc.noaa.gov) There are three major ways to measure precipitation: rain gauges, ground radars and satellites • Other possible ways: • Cell phone network signals (Messer, 2007)

  4. Weather radars and rain gauges (primary source of rainfall) are typically restricted to populated areas on the Earth and can only extend out over water bodies 150 km or so. Satellite-based methodologies serve to fill in these huge data voids, especially over unpopulated regions and oceans.

  5. Satellite-based rainfall estimation methods • Satellite rainfall retrievals are generally categorized into LEO and GEO. • Retrieval algorithms are typically classified on their observing spectrum (VIS, IR, PMW, AMW) or “multi-spectral” (i.e., use of one or more of these individual spectrums). • If the methodology uses multiple satellites or other information such as radar or gauges is classified as a “blended” technique.

  6. Ten widely used high-resolution rain datasets 4 ground-based datasets 6 satellite-based datasets

  7. Winter -- Gauge and radar-based estimates are similar and have small biases. Satellite-based data underestimated in the West. Total biases (mm) for DJF 2006 (2005-06 winter)

  8. Summer -- Gauge and radar-based estimates are similar and have small biases. Satellite-based data overestimated in the central U.S. Total biases (mm) JJA 2006 (2006 summer)

  9. VIS/IR Methods

  10. re re(h) re(h) re Part I: re profile & LWP estimation Previous Studies of LWP estimation Problem : Assume vertically constant re. re is retrieved from single NIR channel and weighted toward cloud top. • Overestimate LWP when re increased with height (IreP) • Underestimate LWP when re decreased with height (DreP) • Chang and Li’s linear Re profile (re1-top, re2-base) retrieval using 1.6µm, 2.1µm, and 3.7µm, and LWP estimation with re profile

  11. Warm RainEstimation A quick look of A-Train observations • 20:55~23:35 UTC at 01/06/08 over eastern pacific • AMSR-E misses the shallow warm rain, MODIS cloud observation shows correlation with warm rain

  12. Passive Microwave Methods

  13. Passive Microwave (PMW) Techniques • Microwave energy can penetrate clouds, in particular, cirrus clouds • Frequencies from 6 GHz to 190 GHz on most PWM sensors. • Below 20 GHz, emission by precipitation-size drops dominates and ice particles above the rain layer are nearly transparent. • Above 60 GHz, ice scattering dominates and the radiometers cannot sense the rain drops below the freezing layer.

  14. Vertical View of Atmospheric Profiles in Microwave Frequency

  15. Basic Relationship Between PMW Frequency and Rainrates

  16. NOAA / AMSU-B

  17. The Advanced Microwave Sounding Unit (AMSU A and B) AMSU-B AMSU-A: Pixel IFOV = 3.3 IFOV Size (Nadir) = 48 km AMSU-B - MHS: Pixel IFOV = 1.1 IFOV Size (Nadir) = 16 km

  18. 1,2 3 4,5 6,7 SSMI 1,2 3 4,5 6,7 SSMI 1-5 6-7 6-7 The Advanced Microwave Sounding Unit (AMSU A and B) AMSU-B 1-5 AMSU-A: Pixel IFOV = 3.3 IFOV Size (Nadir) = 48 km AMSU-B - MHS: Pixel IFOV = 1.1 IFOV Size (Nadir) = 16 km

  19. Rainfall retrieval using AMSU Ice Cloud Scattering Parameter • Physical retrieval of ice water path (IWP) and particle size (De) using AMSU-B 89 and 150 GHz: • De ~ (89)/(150) • IWP ~ De*(/(89,150)) • IWP to rain rate based on limited cloud model data and comparisons with in situ data: RR = A0 + A1*IWP + A2*IWP2 A B (from Zhao and Weng, 2002)

  20. AMSU Rain-Rate Scattering Approach • Advantages: • Availability of three NOAA POES satellites spaced approximately 4 h apart with a spatial resolution of 16 km at nadir (Metop-A is also incorporated with the same capabilities). • Wider swath than SSM/I sensors. • Moisture channels (not available in SSMI) • Weaknesses: • Lack of low frequency channels with appropriate spatial resolution. • Inability to retrieve rain that has little or no ice (only scattering is available). • Cross-scan characteristics of the instrument (different footprints for different local zenithal angles). • Mixed polarization (SSMI V,H polarization)

  21. AMSU Frequency ratio (rr>0/rr≥0) for April 2005. Upper panel: AMSU retrieval Bottom panel: SSMI GPROF 6.0 SSMI – GPROF 6.0

  22. DOD SSM/I

  23. Comparison of SSMI and SSMI/S Sensors characteristics for those specific channels used in the hydrological product generation

  24. SSM/I Precipitation Product • Develop empirical fits between SSM/I F15 and SSMI/S F16 during period of close overpass times (3/06 – 2/07) • All channels • Stratify via land/ocean; rain/no-rain • RADCAL correction applied to F15 8/06 and forward

  25. NASA TRMM

  26. TRMM Satellite

  27. PR TMI reflectivity profiles TMI footprints

  28. TRMM Sensors Precipitation radar (PR): 13.8 GHz 4.3 km footprint 0.25 km vertical res. 215 km swath Microwave radiometer (TMI): 10.7, 19.3, 21.3, 37.0 85.5 GHz (dual polarized except for 21.3 V-only) 10x7 km FOV at 37 GHz 760 km swath Visible/infrared radiometer (VIRS): 0.63, 1.61, 3.75, 10.8, and 12mm at 2.2 km resolution Additional EOS instruments: CERES (Cloud & Earth Radiant Energy System) 720 km swath LIS (Lightning Imaging Sensor) Launch Date: 11/22/1997 Already achieved 10 yr mission

  29. Major Characteristics of TRMM

  30. Major Characteristics of TRMM

  31. 1998-2005 Mean Monthly Rainfall (5°x5°)

  32. Original TRMM Climate Question: How much is it raining in the Tropics (especially over the ocean)? Nine-year TRMM Zonal Average (Ocean [1998-2006]) From 2A12 (TMI[passive microwave]), 2A25 (PR[radar]), and 2B31 (TMI&PR) 220 TRMM Mean 200 TRMM Maximim TRMM Minimum 180 160 140 Precipitation (mm/month) 120 100 80 60 40 20 0 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 Latitude Adler and Wang

  33. TRMM Data Used for Hurricane/Typhoon Monitoring TRMM TMI data used by U.S. and international weather agencies for tropical cyclone detection, location and intensity estimation--600 TRMM-basedtropical cyclone “fixes”per year TRMM orbit advantageous for tropical cyclone monitoring--it is always in tropics, sampling best in 10-35º latitude storm band. TMI resolution twice as good as operational sensors, about same as AMSR. Precessing orbit provides off-time observations relative to sun-synchronous microwave observations. Hurricane Katrina TRMM image from U.S. Navy Tropical Cyclone web site Hurricane Katrina-2005 TRMM radar (PR) cross-sections of hurricanes available in real-time for operational analysis from TRMM web site from TRMM web site

  34. TRMM Precipitation Radar Views Typhoon Etau Only Space-Based Instrument that Provides Vertical Structure in Tropical Rain Systems 13-km tall hot towers Intense convective rains in deep eyewall towers power intensification of Etau, through latent heat release.

  35. OBJECTIVES • Understand horizontal & vertical structure of rainfall, its macro- & micro-physical nature, & its associated latent heating • Train & calibrate retrieval algorithms for constellation radiometers • OBJECTIVES • Provide sufficient global sampling to significantly reduce uncertainties in short-term rainfall accumulations • Extend scientific and societal applications GPM Reference Concept Core Constellation • Core Satellite • TRMM-like spacecraft (NASA) • H2-A rocket launch (NASDA) • Non-sun-synchronous orbit • ~ 65° inclination • ~400 km altitude • Dual frequency radar (NASDA) • K-Ka Bands (13.6-35 GHz) • ~ 4 km horizontal resolution • ~250 m vertical resolution • Multifrequency radiometer (NASA) • 10.7, 19, 22, 37, 85, (150/183 ?) GHz V&H • Constellation Satellites • Pre-existing operational-experimental & dedicated satellites with PMW radiometers • Revisit time • 3-hour goal at ~90% of time • Sun-synch & non-sun- synch orbits • 600-900 km altitudes • Precipitation Validation Sites for Error Characterization • Select/globally distributed ground validation “Supersites” (research quality radar, up looking radiometer-radar-profiler system, raingage-disdrometer network, & T-q soundings) • Dense & frequently reporting regional raingage networks • Precipitation Processing Center • Produces global precipitation products • Products defined by GPM partners

  36. Ground-based Precipitation Radar

  37. Rain and SnowObservable Characteristics Precipitation rate - R (intensity)is the volume flux of precipitation through a horizontal area. In cgs units, R is expressed as cm3 cm-2 sec-1. However, R is usually expressed in mm/h. R is sometimes called the rainfall rate or equivalent rainfall rate. R varies from trace amounts up to several hundred mm/h. R for snow tends to be about 0.1 Rrain.

  38. Rainfall Rate and Drop-Size Distribution Function Where N(D)dD - the number of drops per unit volume with diameters between D and D + dD, V - the fall velocity of drops of size D. For snow, D is the melted diameter of a drop, and R is the equivalent rainfall rate.

  39. Precipitation Water Content The precipitation water content L is independent of the fall speed and is measured in terms of mass/volume

  40. Weather Radar Radar - acronym for RADio Detection and Ranging Main components are: • Transmitter which generates short pulses of electromagnetic energy • Antenna which focuses the energy into a narrow beam • Receiver which detects that portion of the transmitted energy that has been reflected (scattered) by objects with refractive characteristics different from air

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