Estimativa de Precipitação por Satélite. Parte II - Microondas Passivas e Metodologias Combinadas

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1 Estimativa de Precipitação por Satélite Parte II - Microondas Passivas e Metodologias Combinadas Daniel Vila (daniel.vila@cptec.inpe.br) División de Satélites y Sistemas Ambientales Centro de Previsión del Tiempo y Estudios Climáticos Instituto Nacional de Investigaciones Espaciales 24 de Outubro de 2011 Universidade Federal de Rio de Janeiro

2 SUMARIO Introducción: Principios básicos relacionados con la estimación de precipitación a través de satélites meteorológicos Metodologías basadas en satélites geoestacionarios: infrarrojo y visible Metodologías basadas en satélites de orbita polar: microondas pasivas y técnicas combinadas. Metodologías basadas en microondas activas: TRMM y el concepto GPM (Global Precipitation Measurement)

3 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.

4 PMW Techniques Passive MW (PMW) means naturally emitted radiation from the Earth s surface and atmosphere that interacts with clouds and precipitation and is measured by a radiometer on-board a satellite. Most passive microwave radiometers launched to date operate in frequencies ranging from 6 GHz to 190 GHz. 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. Emission-based: are mostly applicable over ocean because water surfaces are relatively homogeneous and provide a cold background due to low emissivity. Scattering-based: Due to the higher, but more varying emissivity of the land surface, the only reliable means of detecting rainfall over land is by isolating depressed TB s as a result of scattering by millimeter sized ice particles that exist in most rain clouds.

5 ~ T s + (1- )T n ~ T s ~ (1- )T n A superfície da Terra emite radiação em microondas. Esta energia interage com os hidrometeoros: absorção, emissão e espalhamento. Como esta interação depende da Freq., podemos inferir algumas propriedades físicas da nuvem amostrada, ou seja: Podemos identificar tipos de hidrometeoros: Água liquida, Gelo, Neve.

6 Na baixa freqüência de microondas ( < 50 GHz), encontra-se a janela atmosférica e estas freqüências são essencialmente sensitivas a emissão do vapor d água, nuvens, precipitação e propriedades da terra.

7 As freqüências altas de microondas são úteis para detectar os efeitos de espalhamento que envolvem precipitação congelada, neve. Enquanto que a absorção e a emissão das gotas de chuva e nuvem aumentam o efeito de espalhamento começa a dominar.

8 PMW Techniques Cumulonimbus Emission freq s <40 GHz Scattering freq s >60 GHz 0 C Nimbostratus 0 C 0 C 0 C

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10 PMW Techniques Estimativa de Precipitacão por Satélite

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27 LEO Temporal Sampling 3-hour slice of data Microwave TMI - white (mm/h) SSM/I 00Z 25 - light May 2004 gray AMSR - medium gray AMSU - dark gray (extracted from Huffman et al, 2001)

28 Special Sensor Microwave/Imager (SSM/I)

29 Special Sensor Microwave/Imager (SSM/I) Physical Retrievals vs. Empirical Methods Goddard Profiling Algorithm (GPROF) Based on Cloud Structures that may be seen by PMW sensors and statistical inversion techniques. Microwave Integrated Retrieval System (MIRS) Based on an assimilation-type scheme (1DVAR) capable of optimally retrieving atmospheric and surface state parameters simultaneously. Ferraro algorithm Utilize both emission and scattering signatures of rainfall over ocean Rainfall detection via scattering 85/89 GHz over land. TB depressions indicate possible rain. Larger the depression, heavier rain

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32 Goddard Profiling Algorithm (GPROF) Estimativa de Precipitacão por Satélite

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34 27 perfis verticais de nuvens: - 18 convectivos e 9 estriformes, definidas em 5 camadas. - Diferentes níveis de congelamento (isoterma de 0 o C), entre 4.5 e 2 km de altura, com intervalos de 0.5 km; - A camada mais baixa até a isoterma de 0 o C, é sempre assumida como sendo liquida; - Camadas mais altas contém estruturas misturadas (hidrometeoros líquidos, congelados e resfriados) dependendo da sua natureza. As convecticas são definidas como gotas liquidas (água de nuvem e chuva ou ambas). As estratiformes não tem água liquida.

35 Precipitação sobre o continente: Como o efeito de emissão é minimizado sobre a terra, somente o efeito de espalhamento de gelo em 85 GHz é observado Nos casos aonde a temperatura de brilho em 85 GHz excede 238 K, a aplicação do algoritmo com o outro canal de espalhamento (37 GHz) fica reduzida, uma vez que a medida esta no limite de ruído das medidas neste canal. Portanto neste algoritmo utiliza-se a relação derivada por Huffman et al. (1993) R(mmh-1) = 0.239(251 T 85H ), T 85H < 247 K Se Tb85H < 238K mas Tb37H > 245 K calcula-se uma média dos perfis verticais convectivo e estratiforme. Sendo que a variância da Tb85H determinará se o pixel será convectivo ou estratiforme, conforme a relação de precipitação de Adler, sendo convectivo para R > 2 mmh- 1 e estratiforme para inferior a este valor. Quanto Tb37H < 245 K, tem-se informação suficiente para aplicar os diferentes canais. O algoritmo é o mesmo que o anterior, sendo que somente o vento de superfície não é variado aleatoriamente.

36 SSMI/S GPROF CPTEC HOME PAGE/CLIMA

37 The Advanced Microwave Sounding Unit (AMSU A and B) 1,2 3 4,5 6,7 SSMI 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

38 Rainfall retrieval using AMSU B A NCEP multisensor hourly rainfall for a winter storm that occurred on 23 Nov 1999, (b) brightness temperature depressions at the cross section of the storm, (from Zhao and Weng, 2002)

39 Rainfall retrieval using AMSU Ice Cloud Scattering Parameter B A where T B (z t, µ) is the upwelling brightness temperature at the cloud top (direct measurement from satellites). Over land T B (z b, µ) is estimated using an empirical relationship between T B31, T B23 and the observed T B (µ). Over the ocean, the atmospheric transmittance, the surface emissivity, surface temperature, and temperature difference between the surface and the atmospheric mean temperature is used instead

40 Rainfall retrieval using AMSU 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)) Assumptions made on sizedistribution & density IWP to rain rate based on limited cloud model data and comparisons with in situ data: RR = A 0 + A 1 *IWP + A 2 *IWP 2 Effects of surface misidentification (desert & snow) reduced using 89 and 150 GHz (from Zhao and Weng, 2002)

41 AMSU CORRECTED SSMI GPROF Estimativa de Precipitacão por Satélite Monthly Precipitation April 2005

43 Blended Techniques are not precipitation estimation techniques but rather methods that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave observations. AMSU-B (NOAA 16,17,18,19) SSM/I (DMSP 13,14 (failed in Oct 08),15 (interference in 22v) TMI (TRMM NASA/Japan) AMSR-E (Aqua) SSMIS (coming soon ) combined with IR frequently updated imagery from GEO satellites.

44 TMPA (TRMM Multi-satellite Precipitation Analysis) Composites of individual overpasses (i.e., instantaneous ) 0.25, 3-hourly - spatial scale > typical satellite pixel size - resolve diurnal cycle Intercalibrate microwave sensors with TRMM Best - TRMM Combined Inst. best vs. atolls over ocean in V.6 - TMI only choice in RT - Apply histogram matching (no constraint on pattern)

45 Calibrate IR with combined microwave - colder clouds rain more histogram matching - no constraint on pattern - not a great concept for instantaneous data - use month-long match-ups for stability - fall-back scheme for cold/frozen land and sea ice where microwave fails Instantaneous SSM/I TRMM AMSR AMSU Hourly IR Tb Calibrate High-Quality (HQ) Estimates to Best Merge HQ Estimates Match IR and HQ, generate coeffs Apply IR coefficients 30-day HQ coefficients 3-hourly merged HQ 30-day IR coefficients Hourly HQ-calib IR precip Combination implemented as IR values filling gaps in microwave coverage - Many other schemes skew the statistics - Data boundaries remain Monthly gauges Merge IR, merged HQ estimates Compute monthly satellite-gauge combination (SG) 3-hourly multi-satellite (MS) Monthly SG Rescale 3-hourly MS to monthly SG Rescaled 3-hourly MS

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47 Description of the CMORPH Technique (Joyce et al., J. Hydromet, 2004) ATTRIBUTES: Analysis of precip. products Spatial scale: 0.07 o lat/lon (60 o N 60 o S) Temporal scale: ½ hour Period of Record: Dec 2002 to present

48 (microwave)?? 1.5 hours apart Derive motion vectors from ½ hourly IR imagery Apply motion to PMW-derived precipitation Premise: Error in using IR to interpolate precip. features identified by PMW is less than the error in deriving precip. directly from IR

49 Advection vectors are computed from IR for each 2.5 O gridbox and all microwave pixels contained in that grid box are propagated in the direction of that vector 2.5 o 2.5 o

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55 Comparison with U.S. Gauge Analyses RADAR Radar CMORPH Merged PMW

56 SALDAS Atmospheric Forcing Datasets The definition and derivation of a 0.125º, 3-hourly atmospheric forcing dataset for the South America continent is described which is appropriate for use in a Land Data Assimilation System and which, because of the limited surface observational networks available in this region, uses remotely sensed data merged with surface observations as the basis for the precipitation and downward shortwave radiation fields; while other variables are model-calculated values from South American Regional Reanalysis (SARR). (i.e., air temperature, wind speed and specific humidity at 2m, surface pressure, etc). REMOTELY SENSED DATASETS & SURFACE OBSERVATIONS DOWNWARD SHORTWAVE RADIATION PRECIPITATION MODEL-BASED DATASETS (SARR) AIR TEMPERATURE WIND SPEED SPECIFIC HUMIDITY, etc.

57 SALDAS rainfall retrieval methodology: Motivation MAIN GOAL: QUANTIFY PRECIPITATION OVER LAND MULTI-SATELLITE APPROACH GAUGE OBSERVATIONS PROS Great spatial and temporal coverage IR/MW blended techniques: a good approach to take advantage of the physically based MW estimates and IR high resolution CONS The performance is highly dependant on the regime and the scale PROS Unique source of direct measurement Largest available records for climate studies CONS Unevenly distributed around the world Sampling and instrumental errors (wind, evaporation, etc.) MERGING APPROACH: THE BEST OF BOTH WORLDS

58 MAIN GOAL: QUANTIFY PRECIPITATION Pluviometer OVER LAND Hydroestimator MULTI-SATELLITE APPROACH MAIN GOAL: QUANTIFY PRECIPITATION GAUGE OBSERVATIONS PROS Great spatial and temporal MULTI-SATELLITE coverage APPROACH IR/MW blended techniques: a PROS good approach to take advantage of Great the physically spatial and based temporal MW estimates coverage and IR high resolution CONS IR/MW blended techniques: a good The performance approach to take is highly advantage dependant of the physically on the based regime MW and the scale estimates and IR high resolution CONS OVER LAND PROS Unique source of direct measurement GAUGE OBSERVATIONS Largest available records for PROS climate studies CONS Unique source of direct measurement Unevenly distributed around the world Largest available records for climate studies Sampling and instrumental CONS errors (wind, evaporation, etc.) Unevenly distributed around the world The performance is highly Sampling and instrumental dependant on the regime MERGING and the APPROACH: THE BEST OF errors (wind, evaporation, etc.) scale BOTH WORLDS MERGING APPROACH: THE BEST OF BOTH WORLDS?

59 SALDAS rainfall retrieval methodology Daily Gauges* Multi-satellite estimate: 3B42RT Mask analysis ADD Bias Removal RAT Bias Removal ADD Selected ADD<RAT RAT Selected Weighting factor: = #ADD/ #Total (3x3 boxes) Block diagram for CoSch satellite-gauge merge process. * ~ rain gauges daily over South America 3B42RT retained Final Merge Analysis RR = *ADD + (1- )*RAT

60 SALDAS rainfall retrieval methodology Case Study: 26/Mar/2004 (a) Rainfall retrieval for 3B42RT on 26 March (b) Idem (a) after applying CoSch.

61 SALDAS rainfall validation methodology bias (mm) 3B42RT COSCH ADDITIVE RATIO RMSE (mm) Corr bias (mm) RMSE (mm) Corr bias (mm) RMSE (mm) Corr bias (mm) RMSE (mm) Corr bias (mm) 2B42V6 January Aprii Juiy October RMSE (mm) Corr..and a degraded network experiment: 10% of the available gauges are used into the correction scheme bias (mm) 3B42RT COSCH ADDITIVE RATIO RMSE (mm) Corr bias (mm) RMSE (mm) Corr bias (mm) RMSE (mm) Corr bias (mm) RMSE (mm) Corr bias (mm) 2B42V6 January April July October RMSE (mm) Corr

62 SALDAS rainfall validation methodology Annual Mean of Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Threat Score (ETS) and Bias Score for different daily rainfall thresholds.

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70 Obrigado!!!