Algorithm Theoretical Definition Document (ATDD) for product. PR-OBS-4 - Precipitation rate at ground by LEO/MW supported by GEO/IR

Transcription

1 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 1 Italian Meteorological Service Italian Department of Civil Defence Algorithm Theoretical Definition Document (ATDD) for product PR-OBS-4 - Precipitation rate at ground by LEO/MW supported by GEO/IR Zentralanstalt für Meteorologie und Geodynamik Vienna University of Technology Institut für Photogrammetrie und Fernerkundung Royal Meteorological Institute of Belgium European Centre for Medium-Range Weather Forecasts Finnish Meteorological Institute Finnish Environment Institute Helsinki University of Technology Météo-France CNRS Laboratoire Atmosphères, Milieux, Observations Spatiales CNRS Centre d'etudes Spatiales de la BIOsphere Bundesanstalt für Gewässerkunde Hungarian Meteorological Service CNR - Istituto Scienze dell Atmosfera e del Clima Università di Ferrara Institute of Meteorology and Water Management Romania National Meteorological Administration Slovak Hydro-Meteorological Institute Turkish State Meteorological Service Middle East Technical University Istanbul Technical University Anadolu University 30 May 2010

2 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 2 Algorithm Theoretical Definition Document ATDD-04 Product PR-OBS-4 Precipitation rate at ground by LEO/MW supported by GEO/IR INDEX Acronyms The EUMETSAT Satellite Application Facilities and H-SAF Introduction to product PR-OBS Sensing principle Main operational characteristics Architecture of the products generation chain Product development team Processing concept Algorithms description The Morphing processing chain Algorithm validation/heritage Examples of PR-OBS-4 products 12 References 13 List of Tables Table 01 List of H-SAF products 05 Table 02 Development team for product PR-OBS-4 07 Page List of Figures Fig. 01 Conceptual scheme of the EUMETSAT application ground segment 05 Fig. 02 Current composition of the EUMETSAT SAF network (in order of establishment) 05 Fig. 03 The H-SAF required coverage in the Meteosat projection 06 Fig. 04 Flow chart of the LEO/MW-GEO/IR-blending precipitation rate processing chain 07 Fig. 05 Graphical representation of the propagation and morphing process in CMORPH 10 through an example in the South Pacific area. The analyses at 03:30 and 05:00 UTC are actual MW estimates, i.e., no propagation or morphing has been applied to these data. The 04:00 and 04:30 UTC are (a) propagated forward in time, (b) propagated backward in time, and (c) propagated and morphed (after Joyce et al. 2004, copyright Amer. Meteor. Soc.) Fig. 06 Morphing of PMW rain products by using the 183-WSL method (Laviola and Levizzani 2008, 2009) during a severe storm over Southern Italy on 02 October Retrieved rain rates at 01:00 UTC are morphed up to the new PMW orbit at 05:00 UTC. Morphing results, mapped on the 8 km grid and regularly updated every 30- min, are shown here for a 1-hour resolution time. Note that last two plates are used to describe the CMORPH reconstruction of rain fields between PMW product as at 05:00 UTC and that at 06:30 UTC, not shown here but indicated by red ovals 13

3 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 3 Acronyms AMSU Advanced Microwave Sounding Unit (on NOAA and MetOp) AMSU-A Advanced Microwave Sounding Unit - A (on NOAA and MetOp) AMSU-B Advanced Microwave Sounding Unit - B (on NOAA up to 17) ATDD Algorithms Theoretical Definition Document AU Anadolu University (in Turkey) BfG Bundesanstalt für Gewässerkunde (in Germany) CAF Central Application Facility (of EUMETSAT) CESBIO Centre d'etudes Spatiales de la BIOsphere (of CNRS, in France) CMORPH Climate Prediction Center (CPC) Morphing method CM-SAF SAF on Climate Monitoring CNMCA Centro Nazionale di Meteorologia e Climatologia Aeronautica (in Italy) CNR Consiglio Nazionale delle Ricerche (of Italy) CNRS Centre Nationale de la Recherche Scientifique (of France) CSAV Cloud System Advection Vectors DMSP Defence Meteorological Satellite Program DPC Dipartimento Protezione Civile (of Italy) EARS EUMETSAT Advanced Retransmission Service ECMWF European Centre for Medium-range Weather Forecasts EDC EUMETSAT Data Centre, previously known as U-MARF EUM Short for EUMETSAT EUMETCast EUMETSAT s Broadcast System for Environmental Data EUMETSAT European Organisation for the Exploitation of Meteorological Satellites FMI Finnish Meteorological Institute FTP File Transfer Protocol GEO Geostationary Earth Orbit GRAS-SAF SAF on GRAS Meteorology H-SAF SAF on Support to Operational Hydrology and Water Management IFOV Instantaneous Field Of View IMWM Institute of Meteorology and Water Management (in Poland) IPF Institut für Photogrammetrie und Fernerkundung (of TU-Wien, in Austria) IR Infra Red IRM Institut Royal Météorologique (of Belgium) (alternative of RMI) ISAC Istituto di Scienze dell Atmosfera e del Clima (of CNR, Italy) ITU İstanbul Technical University (in Turkey) LATMOS Laboratoire Atmosphères, Milieux, Observations Spatiales (of CNRS, in France) LEO Low Earth Orbit LSA-SAF SAF on Land Surface Analysis Météo France National Meteorological Service of France MetOp Meteorological Operational satellite METU Middle East Technical University (in Turkey) MHS Microwave Humidity Sounder (on NOAA 18 and 19, and on MetOp) MSG Meteosat Second Generation (Meteosat 8, 9, 10, 11) MW Micro Wave NESDIS National Environmental Satellite, Data and Information Services NMA National Meteorological Administration (of Romania) NOAA National Oceanic and Atmospheric Administration (Agency and satellite) NOAA National Oceanic and Atmospheric Administration (Agency and satellite) NWC-SAF SAF in support to Nowcasting & Very Short Range Forecasting NWP-SAF SAF on Numerical Weather Prediction O3M-SAF SAF on Ozone and Atmospheric Chemistry Monitoring OMSZ Hungarian Meteorological Service

4 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 4 OSI-SAF Pixel PMW PP PUM PVR RMI SAF SEVIRI SHMÚ SSM/I SSMIS SYKE TKK TMPA TRMM TSMS TU-Wien UKMO U-MARF UniFe URD UTC VIS ZAMG SAF on Ocean and Sea Ice Picture Element Passive Micro-Wave Project Plan Product User Manual Product Validation Report Royal Meteorological Institute (of Belgium) (alternative of IRM) Satellite Application Facility Spinning Enhanced Visible and Infra-Red Imager (on Meteosat from 8 onwards) Slovak Hydro-Meteorological Institute Special Sensor Microwave / Imager (on DMSP up to F-15) Special Sensor Microwave Imager/Sounder (on DMSP starting with S-16) Suomen ympäristökeskus (Finnish Environment Institute) Teknillinen korkeakoulu (Helsinki University of Technology) TRMM Multisatellite Precipitation Analysis Tropical Rainfall Measuring Mission Turkish State Meteorological Service Technische Universität Wien (in Austria) United Kingdom Met Office Unified Meteorological Archive and Retrieval Facility University of Ferrara (in Italy) User Requirements Document Universal Coordinated Time Visible Zentralanstalt für Meteorologie und Geodynamik (of Austria)

5 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 5 1. The EUMETSAT Satellite Application Facilities and H-SAF The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is part of the distributed application ground segment of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The application ground segment consists of a Central Application Facility (CAF) and a network of eight Satellite Application Facilities (SAFs) dedicated to development and operational activities to provide satellitederived data to support specific user communities. See Fig. 01. EUM Geostationary Systems Data Acquisition and Control Data Processing EUMETSAT HQ Systems of the EUM/NOAA Cooperation other data sources Application Ground Segment Meteorological Products Extraction EUMETSAT HQ Archive & Retrieval Facility (Data Centre) EUMETSAT HQ Satellite Application Facilities (SAFs) Centralised processing and generation of products Decentralised processing and generation of products USERS Fig Conceptual scheme of the EUMETSAT application ground segment. Fig. 02 reminds the current composition of the EUMETSAT SAF network (in order of establishment). Nowcasting & Very Short Range Forecasting Ocean and Sea Ice Ozone & Atmospheric Numerical Weather Climate Monitoring Chemistry Monitoring Prediction GRAS Meteorology Land Surface Analysis Operational Hydrology & Water Management Fig Current composition of the EUMETSAT SAF network (in order of establishment). The H-SAF was established by the EUMETSAT Council on 3 July 2005; its Development Phase started on 1 st September 2005 and ends on 31 August The list of H-SAF products is shown in Table 01. Table 01 - List of H-SAF products Code Acronym Product name H01 PR-OBS-1 Precipitation rate at ground by MW conical scanners (with indication of phase) H02 PR-OBS-2 Precipitation rate at ground by MW cross-track scanners (with indication of phase) H03 PR-OBS-3 Precipitation rate at ground by GEO/IR supported by LEO/MW H04 PR-OBS-4 Precipitation rate at ground by LEO/MW supported by GEO/IR (with flag for phase) H05 PR-OBS-5 Accumulated precipitation at ground by blended MW and IR H06 PR-ASS-1 Instantaneous and accumulated precipitation at ground computed by a NWP model H07 SM-OBS-1 Large-scale surface soil moisture by radar scatterometer H08 SM-OBS-2 Small-scale surface soil moisture by radar scatterometer H09 SM-ASS-1 Volumetric soil moisture (roots region) by scatterometer assimilation in NWP model H10 SN-OBS-1 Snow detection (snow mask) by VIS/IR radiometry H11 SN-OBS-2 Snow status (dry/wet) by MW radiometry H12 SN-OBS-3 Effective snow cover by VIS/IR radiometry H13 SN-OBS-4 Snow water equivalent by MW radiometry

6 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 6 2. Introduction to product PR-OBS Sensing principle Product PR-OBS-4 (CMORPH) is based on the same ingredients of PR-OBS-3, but the blending methodology is radically different. Precipitation rates (PR-OBS-1 and PR-OBS-2 in the H-SAF framework) are not used to calibrate infrared brightness temperatures as for PR-OBS-3 but they are advected by using motion vectors calculated on the basis of cloud-top temperatures inferred at 10.8 μm. Specifically, GEO-IR information is just used to propagate the microwave-derived precipitation features when passive microwave (PMW) data are not available at a given location. By considering two different but consecutive PMW images, converted to rain rates, on the basis of brightness temperatures on top of rainy clouds as Fig The H-SAF required coverage in the Meteosat projection. observed by the 10.8 μm MSG-SEVIRI channel, a series of advection vector matrices are calculated. This process governs the movement of the precipitation features only. At a given location, the shape and intensity of the precipitation features are determined by performing a time-weighting interpolation between microwave-derived features that have been propagated forward in time from the previous PMW observation and those that have been propagated backward in time from the following PMW scan. Note that the CMORPH method does not extrapolate information from current precipitation field toward the future but it mathematically interpolates rain rates between two consecutive events. The product is generated at 30-min rate (from the 15-min imaging rate of SEVIRI), and the spatial resolution of the final product in remapped on a 8- km grid map. The processing method is called Morphing (Joyce et al. 2004). The processing area is the same as for PR-OBS-3 and PR-OBS-5 (see Fig. 03) For more information, please refer to the Products User Manual (specifically, PUM-04). 2.2 Main operational characteristics The operational characteristics of PR-OBS-4 are discussed in PUM-04. Here are the main highlights. The horizontal resolution (x). The effective resolution is controlled by the PMW-derived products, PR-OBS-1 and PR-OBS-2, therefore a figure representative of the PR-OBS-4 resolution is: x ~ 30 km. However, the morphing procedure operates on a fixed grid of ~ 8 km intervals, close to the SEVIRI IFOV over Europe. Conclusion: resolution x ~ 30 km - sampling distance: ~ 8 km. The observing cycle (t). The composite observing cycle of PR-OBS-1 + PR-OBS-2 over Europe is about 3 h. PR-OBS-4 is run over one area when at least two of the rain products PR-OBS-1 and/or PR- OBS-2 are stored. Interpolated maps are available at 30 min intervals. Conclusion: observing cycle t ~ 3 h - sampling time: 30 min. The timeliness (). At any time within the ~ 3-h interval between two MW images the output will have to wait for the second PMW image, therefore the average distance from the output time will be ~ 90 min. Adding ~ 30 min for the timeliness of the last image, we have timeliness ~ 120 min. The accuracy is evaluated a-posteriori by means of the validation activity. See Product Validation Report PVR-04.

7 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page Architecture of the products generation chain The architecture of the PR-OBS-4 product generation chain is shown in Fig. 04. SSM/I-SSMIS AMSU-MHS ~ 3-hourly sequence of MW observations Morphing algorithm SEVIRI 15-min images Lookup tables updating Rapid-update algorithm PRECIPITATION RATE Extraction of dynamical info Actually, Fig. 04 refers to the architecture of the coupled PR-OBS-3 and PR-OBS-4 products, that includes: the Rapid Update process based on (frequent) SEVIRI IR images calibrated by the (infrequent) PMW-derived precipitation data as retrieved from SSM/I and SSMIS (PR-OBS-1) or from AMSU- A, AMSU-B or MHS (PR-OBS-2); the Morphing process based on (infrequent) PMW-derived precipitation maps, and PMW precipitation pseudo-maps interpolated at frequent intervals by exploiting the dynamic information provided by the SEVIRI images. It is noted that, at the time of the ORR in mid-2010, the Morphing-based product (PR-OBS-4) has not yet reached a potentially pre-operational status; PR-OBS-3 does not yet make use of PMW precipitation data coming from the PR-OBS-1 chain. 2.4 Product development team Names and coordinates of the main actors for PR-OBS-4 algorithm development and integration are listed in Table 02. Vincenzo Levizzani (Leader) Sante Laviola Elsa Cattani Fig Flow chart of the LEO/MW-GEO/IR-blending precipitation rate processing chain. Table 02 - Development team for product PR-OBS-4 CNR Istituto di Scienze dell Atmosfera e del Clima (ISAC) Italy v.levizzani@isac.cnr.it s.laviola@isac.cnr.it e.cattani@isac.cnr.it

8 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 8 3. Processing concept The PR-OBS-04 product is based on PMW-derived precipitation measurements generated as PR-OBS-1 and PR-OBS-2, and IR images from the geostationary Meteosat satellites namely SEVIRI on Meteosat 8 (Meteosat Second Generation, MSG). The operational characteristics of PR-OBS-1 are discussed in PUM-01. Here are the main highlights. Product source - SSM/I and SSMIS PMW radiometers flown on satellites of the DMSP series. Main instrument features - Multi-purpose imaging PMW radiometer - 4 frequencies, 7-channels. Conical scanning, 53.1 zenith angle, swath 1400 km (SSM/I) or 1700 km (SSMIS) - Scan rate: 31.9 scan/min = 12.5 km/scan. Horizontal resolution (x) - The IFOV of SSM/I-SSMIS images changes with frequency from ~ 13 km at 90 GHz to ~ 55 km at 19 GHz). The PR-OBS-1 product has: - resolution x ~ 30 km - sampling distance: ~ 16 km. Observing cycle (t) - Depends on the instrument swath and the number of satellites carrying SSM/I or SSMIS. For the PR-OBS-1 product we have: - observing cycle t = 6 h - sampling time: 210 h. Timeliness () - Strongly conditioned by the availability of DMSP data at CNMCA, through NOAA and UKMO. The outcome is - timeliness ~ 2.5 h. The operational characteristics of PR-OBS-2 are discussed in PUM-02. Here are the main highlights: Product source - AMSU-A and AMSU-B or MHS MW radiometers flown on satellites of the NOAA and MetOp series. Main instrument features - Temperature (AMSU-A) or humidity (AMSU-B and MHS) PMW sounders in nearly-all-weather conditions. 15 channels (AMSU-A) and 5 channels (AMSU-B and MHS) operating in absorption bands of O 2 (AMSU-A) or H 2 O (AMSU-B and MHS). Cross-track scanning, swath 2250 km; AMSU-A by 30 steps of 48 km s.s.p., along-track: one 48-km line every 8 s; AMSU-B and MHS by 90 steps of 16 km s.s.p., along-track: one 16-km line every 8/3 s. Horizontal resolution (x) - The AMSU-A is submitted to resolution enhancement by blending with AMSU/B/MHS. The PR-OBS-3 product has: - resolution x ~ 40 km - sampling distance: ~ 16 km. Observing cycle (t) - Depends on the instrument swath and the number of satellites carrying AMSU-A and AMSU-B or MHS. For the PR-OBS-2 product we have: - observing cycle t = 6 h - sampling time: h. Timeliness () - Direct-read-out and EARS/EUMETCast acquisition are provided. We have: - timeliness ~ 0.5 h. It is important to note that we have: for the composite PR-OBS-1 + PR-OBS-2 system: observing cycle t = 3 h, sampling 24.5 h. Pseudo precipitation maps are interpolated by exploiting dynamical information from SEVIRI, that is: Multi-purpose imaging VIS/IR radiometer - 12 channels (11 narrow-bandwidth, 1 high-resolution broad-bandwidth VIS) 4.8 km IFOV, 3 km sampling for narrow channels; 1.4 km IFOV, 1 km sampling for broad VIS channel image cycle 15 min (actually 30 min used for PR-OBS-4).

9 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 9 4. Algorithms description The following Sections describe the algorithms used in the various modules of the precipitation products generation chain. The degree of detail is consistent with the requirement of a manageable document. Detailed algorithm descriptions are available within the H-SAF project via an electronic forum at the site: ftp://ftp.meteoam.it - username: hsaf - password: 00Hsaf hsaf algorithm-forum. 4.1 The Morphing processing chain Recently a method alternative to the Rapid Update (product PR-OBS-3) for combining GEO/IR radiances and LEO/PMW rainfall estimates has been proposed by Joyce et al using an entirely different combination concept. As for the Rapid-update algorithm, precipitation estimates are derived from LEO PMW observations whose representation of the physics of precipitation formation mechanisms is of higher quality. The features of the PMW precipitation estimates are then transported via information on the spatial propagation of precipitating systems obtained from GEO/IR data during periods when instantaneous PMW data are not available at a given location. The technique is labelled CMORPH, acronym of the Climate Prediction Center (CPC) morphing method. Propagation vector matrices are produced by computing spatial lag correlations over successive images of GEO/IR and then used to propagate the PMW-derived precipitation estimates in time and space when updated PMW data are unavailable. This process governs the movement of the precipitation features only. At a given location, the shape and intensity of the precipitation features in the intervening halfhour periods between PMW scans are determined by performing a time weighted interpolation between PMW-derived estimates that have been propagated forward in time from the last available PMW observation and those that have been propagated backward in time from the next available PMW scan. This latter step, referred to as morphing of the features, produces spatially complete analyses of precipitation every half hour. As it is the case for the Rapid-update algorithm, the method is flexible in that any precipitation estimates from any PMW satellite source can be incorporated. At the time of writing of the present report, the precipitation estimation algorithms incorporated in CMORPH are those of NOAA-NESDIS (Ferraro 1997; McCollum and Ferraro 2003; Laviola and Levizzani 2008, 2009). The latest versions of the algorithm for conical scanning systems contains modifications that essentially eliminate the global high bias found in previous versions of the SSM/I and TMI algorithms. However, many regional and seasonal biases still exist and they are identified by Mc Collum and Ferraro IR data are known to provide good measurements of cloud-top properties and thus they can be used to detect cloud systems and to determine their movement. In particular, they are commonly used to derive cloud system advection vectors (CSAVs). Note that, however, the direction and speed of cloud tops as detected by satellite IR imagery may not always correlate well with the propagation of the lower precipitating layer of the system, and wind direction changes and wind speed generally increase in magnitude with height from the Earth s surface. Joyce et al have experimented that spatially lagging and overlapping 5 latitude/longitude IR regions centred at 2.5 latitude/longitude intervals provide a good measure of the movement of entire cloud systems. Adjustment factors for the zonal and meridional propagation patterns of precipitating systems as well as for seasonal patterns based on radar systematic observations are also included. The PMW rainfall propagation process begins by spatially propagating the initial field of 8-km halfhourly instantaneous PMW analysis estimates (t + 0 h) forward in time, by the discrete distance of the corresponding zonal and meridional vectors. The time stamp (t = 0 for instantaneous), in which the units represent the time, in ½-hourly increments, since the scan of the PMW satellite overpass used to define that pixel, and 2) the satellite identification are retained with each precipitation estimate. All PMW satellite pixels within each 2.5 latitude/longitude box are propagated in the same direction and distance to produce the analysis for the next half hour (t h). Caution is devoted to special cases: a) the rainfall field is propagated evenly if the vector pairs from two of the boxes match exactly in the case of a PMW rainfall feature on the border between the two boxes; b) the average of the values is

10 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 10 computed when two pixels from different regions are propagated to the same pixel location by convergence; c) when a data gap in the rainfall field is created by divergence a bilinear interpolation of the rainfall features across the gap is computed; d) when a MW-derived precipitation estimate from a new scan at t h is available at a particular pixel location, this latter overwrites the propagated estimate and the associated time stamp for that pixel is set to a value of zero. Otherwise, the time stamp is incremented by a value of 1. The above process is repeated each half hour. The propagation process is illustrated in Fig. 05 from Joyce et al Fig Graphical representation of the propagation and morphing process in CMORPH through an example in the South Pacific area. The analyses at 03:30 and 05:00 UTC are actual MW estimates, i.e., no propagation or morphing has been applied to these data. The 04:00 and 04:30 UTC are (a) propagated forward in time, (b) propagated backward in time, and (c) propagated and morphed (after Joyce et al. 2004, copyright Amer. Meteor. Soc.).

11 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 11 An initial 03:30 UTC time analysis of instantaneous PMW rainfall (t + 0 h) consisting of two clusters over a region in the South Pacific is propagated forward to produce analyses at t and t + 1 h (Fig. 05.a) using the IR-derived propagation vectors. The instantaneous rainfall analyses are at the same time spatially backward propagated (Fig. 05.b) using the same propagation vectors used in the forward propagation, just by reversing the sign of those vectors, and the results stored separately from those stemming from the forward propagation. In Fig. 05.b the t h updated observed PMW precipitation is propagated backward to the t + 0 h time frame. When all propagated fields have been computed, the t + 0 h analysis that contains observed data overwrites the propagated estimates for that time stamp. Note that, however, a simple propagation of the precipitation features themselves will obviously not account for any change in the character of those features but merely translate them to new locations. CMORPH tries to follow these changes in the intensity and shape of the rainfall field through the inverse weighting of both forward- and backward-propagated rainfall by the respective temporal distance from the initial and updated observed analyses. This process is here referred to as morphing and gives the name to the technique (see Fig. 05.c). At each pixel location, the process of production of the 04:00 UTC (t + ½ h) estimate (Fig. 05.c, second column) is based on the following weighted mean: Morphed Value (t + ½ h) = 0.67 P forward(t + ½ h) P backward(t + ½ h) where P forward is the PMW precipitation estimate forward propagated from the initial analysis (03:30 UTC), and P backward is the PMW precipitation estimate backward propagated from the updated analysis (05:00 UTC). Similarly, the CMORPH value for the 04:30 UTC analysis is computed as follows: Morphed Value (t + 1 h) = 0.33 P forward(t + 1 h) P backward(t + 1 h) 4.2 Algorithm validation/heritage The method was initially conceived for climate applications at NOAA s Climate Prediction Center and is extensively used to produced global precipitation datasets for climate monitoring ( Regional precipitation estimates are operationally produced over various areas of the globe such as, for example, Central America ( The method has been mainly used insofar as a scientific tool to explore the global precipitation distribution, diurnal cycles and, in general, for exploring the atmospheric component of the water cycle. Among others, Janowiak et al have based on routinely produced CMORPH global data their analysis of the diurnal cycle of precipitation both regionally and globally by dwelling on the 8-km resolution of the rain fields. Pereira et al have recently assessed the possible use of CMORPH for the management of large watersheds in South America reaching the conclusion that the product shows considerable potential. Validation studies of the method were conducted over various areas of the globe. Ebert et al have assessed the accuracy of the CMORPH products at daily time scales and degree spatial scales over Australia and the United States. They concluded that: 1) during the warm season, the frequency bias (number of rainy days from CMORPH divided by the corresponding value from a network of rain gauges) varies from 0.4 to 2.5 (with a median of 1.1) for Australian midlatitude and from 0.6 to 1.1 (with a median of 0.9) for Australian tropics during the 2-year period ; 2) during the cold season, the frequency bias varies from 0 to 0.9 (with a median of 0.4) for Australian midlatitude and from 0 to 0.8 (with a median of 0.25) for Australian tropics; 3) the spatial correlation between CMORPH and ground based radar precipitation estimates varies mostly between 0.1 and 0.4 in the US. Tian et al evaluated the accuracy of CMORPH and other products at daily, 0.25 degree scales across the US, by comparing them with ground measurements (radar and rain gauges). In the warm seasons, CMORPH significantly overestimates precipitation across the vast majority of the US, and the overestimation is severe in the central US (Nebraska, Iowa, Kansas, and Oklahoma). In Oklahoma the overestimation varies in the range of 2 4 mm day -1, which roughly translates into 28 % 58 % of the seasonal mean. In the cold seasons, CMORPH tends to slightly underestimate precipitation over the majority of the regions.

12 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 12 Zeweldi and Gebremichael 2009 have performed a validation exercise over the Little Washita river watershed in Oklahoma. Although their results are not conclusive, the authors show that 1) the performance statistics of CMORPH displays a high degree of variability from one hour to the next, suggesting that the performance statistics are dynamic in time; 2) CMORPH spatial fields tend to be smoother than the radar output; 3) the errors are temporally correlated, in particular within the range from 1 to 6 accumulation hours, implying that averaging CMORPH products over these time scales does not reduce the errors significantly; and 4) the errors become less correlated in time as the averaging time scale increases from 6 to 24 h. However, at the global scale the results of the intercomparison conducted by Sapiano et al using high-resolution precipitation datasets have shown correlations against 3-hourly gauge data as high as 0.7 for CMORPH, which had the highest correlations with the validation data. Dinku et al have compared the CMORPH and TMPA (TRMM Multisatellite Precipitation Analysis) products over two mountainous regions and at daily accumulation and spatial resolution The two validations regions were located over the Ethiopian highlands in the Horn of Africa and over the highlands of Columbia in South America. Both sites are characterized by a very complex terrain with relatively dense station networks. CMORPH has exhibited better performance as compared to the two TRMM products. An ongoing extensive validation activity at several sites over the globe is carried out by the IPWG and can be monitored at

13 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page Example of PR-OBS-4 products At the time of this writing, PR-OBS-4 is not yet generated on a regular basis. A prototype example is provided in Fig. 06. PMW at 01:00 UTC Morphing at 01:30 UTC Morphing at 02:30 UTC Morphing at 03:30 UTC PMW at 04:00 UTC Morphing at 04:30 UTC PMW at 05:00 UTC Morphing at 05:30 UTC Morphing at 06:30 UTC Fig Morphing of PMW rain products by using the 183-WSL method (Laviola and Levizzani 2008, 2009) during a severe storm over Southern Italy on 02 October Retrieved rain rates at 01:00 UTC are morphed up to the new PMW orbit at 05:00 UTC. Morphing results, mapped on the 8 km grid and regularly updated every 30-min, are shown here for a 1-hour resolution time. Note that last two plates are used to describe the CMORPH reconstruction of rain fields between PMW product as at 05:00 UTC and that at 06:30 UTC, not shown here but indicated by red ovals.

14 Algorithms Theoretical Definition Document, 30 May ATDD-04 (Product PR-OBS-4) Page 14 References Dinku T., S.J. Connor and P. Ceccato, 2010: Comparison of CMORPH and TRMM-3B42 over Mountainous Regions of Africa and South America. In: Satellite rainfall applications for surface hydrology, M. Gebremichael, and F. Hossain, Eds., Springer, Ebert E.E., J. Janowiak and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, Ferraro R.R., 1997: Special sensor microwave imager derived global rainfall estimates for climatological applications. J. Geophys. Res., 102 (D14), Janowiak J.E., V.E. Kousky and R.J. Joyce, 2005: Diurnal cycle of precipitation determined from the CMORPH high spatial and temporal resolution global precipitation analyses. J. Geophys. Res., 110, D23105, doi: /22005jd Joyce R.J., J.E. Janowiak, P.A Arkin and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, Laviola S. and V. Levizzani, 2008: Rain retrieval using the 183 GHz absorption lines. IEEE Proc. MicroRad 2008, p. doi: /micrad Laviola S. and V. Levizzani, 2009: Observing precipitation by means of water vapor absorption lines; a new approach to retrieve rain rates from satellite. Italian J. Rem. Sensing, 41(3), McCollum J.R. and R.R. Ferraro, 2003: Next generation of NOAA/NESDIS TMI, SSM/I, and AMSR- E microwave land rainfall algorithms. J. Geophys. Res., 108, Pereira A.J., R.E. Carbone, J.E. Janowiak, P. Arkin, R. Joyce, R. Hallak and C.G.M. Ramos, 2010: Satellite rainfall estimates over South America - Possible applicability to the water management of large watersheds. J. Amer. Water Resour. Ass., 46, Sapiano M.R.P. and P.A. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, Tian Y., C.D. Peters-Lidard, B.J. Choudhury and M. Garcia, 2007: Multitemporal analysis of TRMMbased satellite precipitation products for land data assimilation applications. J. Hydrometeor., 8, Zeweldi D.A. and M. Gebremichael, 2009: Evaluation of CMORPH precipitation products at fine space time scales. J. Hydrometeor., 10,