The Algorithm Intercomparison Project utilises rainfall estimates derived from radar data to validate the algorithms developed for rainfall retrievals from satellite microwave data. Since seven minutes are needed in order to have a complete radar scan, while the acquisition of the corresponding satellite microwave image needs only a few seconds, the same pixel can be sensed by radar as much as

seven minutes later. Within this time delay the raining cells can be displaced and the consequent mismatch can cause a decrease in the correlation coefficient of the

comparison. A method to reveal this time-lag effect is presented and a possible approach to take it into account in the validation process for future missions is suggested

Cadeddu, M. P.

Dalu, G.

Prabhakara, C.

Short, D.

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IL NUOVO CIMENTO Gennaio-Febbraio 1998VOL. 21 C, N. 1NOTE BREVIOn the validation of rainfall retrieval algorithmsfor satellite microwave dataG. DALU (1), M. P. CADEDDU (1), C. PRABHAKARA (2) and D. SHORT (2)(1) Area della Ricerca - Via Bottego 7, I-09125 Cagliari, Italy(2) Laboratory for Atmospheres, NASA-GSFC - Greenbelt, MD 20771, USA(ricevuto il 15 Aprile 1997; approvato il 25 Luglio 1997)Summary. — The Algorithm Intercomparison Project utilises rainfall estimatesderived from radar data to validate the algorithms developed for rainfall retrievalsfrom satellite microwave data. Since seven minutes are needed in order to have acomplete radar scan, while the acquisition of the corresponding satellite microwaveimage needs only a few seconds, the same pixel can be sensed by radar as much asseven minutes later. Within this time delay the raining cells can be displaced and theconsequent mismatch can cause a decrease in the correlation coefficient of thecomparison. A method to reveal this time-lag effect is presented and a possibleapproach to take it into account in the validation process for future missions issuggested.PACS 92.60.Jq – Water in the atmosphere (humidity, clouds, evaporation, precipita-tion).1. – IntroductionThe Algorithm Intercomparison Project (AIP) is used to validate the algorithms forthe retrieval of precipitation from radiometric microwave data, by comparison with theprecipitation estimates obtained from the corresponding radar data.The algorithms tested in the AIP-1 (Lee et al., 1991) gave acceptable resultsbecause the comparison was performed on area-time averaged values of theprecipitation. It is, however, desirable to have an algorithm capable of estimating theinstantaneous precipitation on the highest resolution possible, for example 15 km of the85 GHz channel of the Special Sensor Microwave Imager (SSM/I).Most of the algorithms tested in the AIP-3 (Ebert, 1995) used two or morefrequencies in the attempt to make the best use of the information contained in thedata, but if the comparison is performed on a pixel by pixel basis, the quality of theresults is disappointing.The introduction of some additional information based on radiative transfersimulations and on the known cloud physics did not substantially improve the resultsG Società Italiana di Fisica 115G. DALU, M. P. CADEDDU, C. PRABHAKARA and D. SHORT116(Prabhakara et al., 1996). In fact, the correlation coefficients of the comparisons wereso low that made us wonder if we were comparing two completely different sets of data.The radar scan starts from low elevation, and changes zenith angle after every 3607horizontal scan, which takes about 20 seconds. Precipitation is estimated from theradar data obtained at a constant altitude of about 2 km (Battan, 1973).Twenty zenith angles are necessary to obtain an image of the 2 km altitude radardata and a complete image is obtained in seven minutes. The next image starts after tenminutes. The corresponding satellite image is obtained in about 15 seconds. As a result,only a few percent of the pixels are simultaneous, and some of them can be as much asseven minutes apart.The raining cells, and clouds in general, are rapidly evolving phenomena and canchange dramatically in a few minutes. But the highest changes, from heavy rain to norain, is simply due to the fact that the raining cell can be advected by the wind to anadjoining pixel, i.e. the raining cell is not exactly on the same location anymore. Thisexplains why sometimes heavy rain sensed by radar has no rain in the correspondingSSM/I field of view, and viceversa. This type of results reduces the correlationcoefficients in the comparisons for the validation.2. – The time-lag effectSince corresponding pixels can be sensed by the radar and by the satellitemicrowave radiometer with a time-lag as long as seven minutes, it is important to knowif some real change in precipitation has not taken place.To quantify the effect of this time-lag, and of the consequent mismatching, we havecompared the radar images among themselves. In this way, there is no difference in theretrieval method, instrumentation, or measuring techniques. In fact, if we superimposetwo successive radar images taken in a windy day, the displacement of the raining cellsis evident.Comparisons were done as follows. All the images were pre-processed to degradethe spatial resolution to the field of view of the 85 GHz channel, 15 km, which was takenas reference pixel. Then all the pixels with rain rate less than 0.04 mm/h in the firstimage were neglected. This was done because a large population of pixels without raincan artificially increase the correlation coefficient and change the slope of thecomparison. The chosen pixels of the first image were compared with thecorresponding pixels of the image taken ten minutes later, then with the correspondingpixels of the image taken twenty minutes later, and so on.In fig. 1 we present the correlation coefficients in three cases with increasing windvelocity. Starting from the maximum correlation coefficient of 1, obtained by comparingthe first radar image with itself, and taken as a reference image, we have a decrease aslarge as 0.5 in the following image, with corresponding pixels observed only tenminutes later.The upper curve is obtained from the radar data of December 24, 1992, withmoderate wind velocity. The lowest curve is for January 31, 1993, with relative strongwind. In this case the correlation coefficient drops to less than 0.2 in twenty minutes.It is evident that the corresponding satellite and radar data for the validation of aretrieval algorithm have to be simultaneous.The Tropical Rainfall Measuring Mission (TRMM) (Simpson et al., 1996) satellitehas a multichannel microwave radiometer (TRMM Microwave Imager, TMI) and aON THE VALIDATION OF RAINFALL RETRIEVAL ALGORITHMS ETC. 117Fig. 1. – Correlation coefficients as a function of time for three different days with increasing windvelocity, going from the upper graph to the lower graph.Precipitation Radar (PR). However, the TMI has a conical scan and scans ahead of thesub-satellite point, while the radar scans across the sub-satellite point. Given thealtitude of the satellite and its speed, each pixel is scanned by the radar about 1 minutelater. Most probably a one minute delay does not introduce relevant changes in the sizeand position of the raining cells. But the altitude of the TRMM satellite is lowercompared to the one of the SSM/I radiometer and the spatial resolution is increasedfrom 15 to about 5 km in the 85 GHz channel, enhancing the effect of the rain cellsdisplacement.An evaluation of the effect of this time lag can be obtained by modifying the dataacquisition sequence of the TMI, which requires also that the antenna should belocated under the satellite.In the present configuration the data are taken only in the area ahead of thesatellite and the useful swath is about 720 km. The radar scans across the sub-satellitepoint with a swath of about 220 km. Assuming that the quality of the rain rate estimateobtained from the radar data is satisfactory, it is possible to validate the algorithmsdeveloped for the TMI data and extend the retrieved information to a full 720 km swath.With the TMI antenna under the satellite and a new data acquisition sequence,some data in a limited swath of about 100 km in the backward direction can be included.Given the altitude and speed of the TRMM satellite, all the pixels within this limitedswath will be scanned twice in a time interval of about 2.2 minutes, giving a possibleway to quantify the differences in size and location of the raining cells.Using this new approach, it is also possible to have an evaluation of the wind speedand direction. Moreover, when the raining cell is steady in the same position andrelatively stable, a stereographic image of the cloud can be analysed in order to givesome new insight on the cloud structure.G. DALU, M. P. CADEDDU, C. PRABHAKARA and D. SHORT1183. – ConclusionAny validation process requires that the corresponding data are comparable. Insidea thunderstorm the wind can peak to a velocity sufficient to displace a raining cell to anadjoining field of view. The time-lag between the radar rain and the satellite rain canresult in a real change in the precipitation on a single pixel, making the entirevalidation process questionable. Raining cells can change so rapidly in size andlocation, that even comparing two successive radar images taken ten minutes apart cangive a correlation coefficient as low as 0.5.Some improvements are expected from the TRMM mission, since both radar andmicrowave radiometer are on the same satellite. However, we recommend to check thetime-lag effect using the same type of data. This can be obtained by taking somemicrowave radiometric data in the backward direction and by comparing them with thecorresponding data taken in the forward direction.R E F E R E N C E SBATTAN, L. J., Radar Observation of the Atmosphere, 2nd edition (University of Chicago Press,Chicago and London) 1973.EBERT E. E., 1995: Results of the 3rd Algorithm Intercomparison Project (AIP-3) (Bureau ofMeteorology Research Center, Melbourne, Australia) 1995.LEE T. H., JANOWIAK J. E. and ARKIN P. A., Atlas of product from the algorithm intercomparisonproject 1: Japan and surrounding oceanic regions, June-August 1989 (UniversityCorporation for the Atmospheric Research) 1991.PRABHAKARA C., CADEDDU M. P., SHORT D. A., WEINMAN J. A., SCHOLS J. L., HAFERMAN J.,Stratiform and convective rain discrimination from microwave radiometer observations.Proceedings of the International Radiation Symposium, Fairbanks, Alaska, 19-24 August1996, edited by W. L. SMITH and K. STAMNES (A. Deepak Publishing, A Division of Scienceand Technology Corporation, Hampton, Virginia) 1997, pp. 498-501.SIMPSON, J. et al., On the Tropical Rainfall Measuring Mission (TRMM), Meteor. Atmos. Phys.,60 (1996) 19-36.

On the validation of rainfall retrieval algorithms for satellite microwave data

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On the validation of rainfall retrieval algorithms for satellite microwave data

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