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CONTINUOS THUNDERSTORM MONITORING: RETRIEVAL OF PRECIPITATION PARAMETERS FROM LIGHTNING OBSERVATION Carlos Augusto Morales University of Connecticut, Dept. of Civil and Enrionmenal Engeneering, Storrs, CT 06269, USA Emmanouil N. Anagnostou University of Connecticut, Dept. of Civil and Enrionmenal Engeneering, Storrs, CT 06269, USA James A. Weinman GSFC/NASA, Microwave Sensors Branch, Greenbelt, MD20771, USA Abstract: A detail analysis of the relationship between rainfall parameters measured by the Tropical Rainfall Measuring Missing (TRMM) and lightning measurements will be presented. Our ultimate goal is to develop a methodology to retrieve ice content and the associated mean vertical profile of radar reflectivity using combined measurements of lightning, and satellite infrared and passive microwave brightness temperatures. The lightning information is derived from sferics measurements. Sferics is the radio noise emitted by a lightning flash. At very low frequencies ground receivers can detect electrical discharges at distances beyond 3,000 km. During 1997-1998, NASA in conjunction with Resolution Display Inc. installed such sensors and collected continuous sferics measurements over the North and South America. This study will be carried out based on coincident sferics and TRMM data from the above period. An error analysis will be performed to quantify uncertainties associated with the proposed method. 1. Introduction: Information on the spatial and temporal variability of precipitation is of fundamental importance to applications ranging from hydrologic engineering to climate change research. Rainfall measurements can improve water and energy budgets, as well the numerical weather models’ prediction accuracy. Furthermore, policy and decision makers on various aspects of socio-economic activities would need to know the precipitation distribution along large regions to better plan their actions. It is known that two thirds of the Earth’s precipitation falls over the tropics and subtropics. These regions are primarily covered by ocean, tropical rain forests and undeveloped areas where direct precipitation measurements are infrequent and confined at few locations. Subsequently, remote sensing observations are the only available tool to monitor precipitation over these regions. However, remote sensors have limitations, and the challenge has been in developing methodologies to yield reliable quantitative rainfall estimates at suitable spatial and temporal scales. The latest advances in remote sensing technology and computer engineering have lead to improvements in global precipitation monitoring. Despite these advances, the global rainfall estimates are continually constrained at scales of the order of 1x1 to 2.5x2.5-degree grid boxes with time scales ranging from daily to monthly. These constraints are associated with sampling and physical limitations of the sensor measurements. New instruments and technologies are been investigated with purpose to supplement some of these deficiencies. Systems to detect 3723 lightning occurrence over very large areas (several millions of square miles) is a technology that offers valuable indirect rainfall information, which in conjunction with other remote sensing data could be used to improve the rainfall estimation at smaller spatial scales (< 25×25km2) and higher temporal frequencies (< 1hour). Lightning, an electrical phenomenon in the atmosphere, is produced by the collision of ice particles embedded in intense cloud updrafts. These electrical discharges can be measured continuously at large distances by radio receivers (Lee, 1986). Workman and Reynold (1949) related flash rates to convective rain fluxes, and suggested that the frequency of lightning may be a measure of convective activity. Goodman (1990) has developed a relationship between lightning frequency and rainfall intensity for systems in Florida. Similarly, Buechler et al. (1994) have demonstrated a linear relationship between rainfall and lightning activity for Florida thunderstorms. Petersen and Rutledge (1996) in a more generalized study computed ratios of rainfall yelds to cloud-to-ground lightning flash frequency for different parts of the world. A more physical approach for retrieving rainfall rate from lightning would be to relate it to the ice content of the precipitating cloud. Mohr et al (1996) have found that flash rates are inversely correlated to the satellite microwave (85 GHz) brightness temperature observations, which signal depression is associated with the presence of graupel and ice. Illingworth and Lees (1991), who studied the relation between radar reflectivity and flash rates, suggest that lightning is associated with convective clouds containing wet hail. Cheze and Sauvageot (1997) also found a very tight relationship between lightning and average rainfall for convective systems in France using as reference weather radar rainfall data. Finally, Toracinta et al. (1996) have shown that the majority of negative cloud to ground (CG) lightning flashes are located near high-reflectivity convective cores in the mixed-phase region. The ultimate goal of this paper is to present a multi-remote sensor algorithm for continuously (in time) retrieving precipitation parameters over the North and South America. Specifically, the objectives are: I. Investigate the potential of thunderstorm monitoring from combination of lightning, IR, and MW sensors; II. Develop statistical relationships between lightning and rain parameters; III. Develop a multi-stage rain retrieval scheme, which would combine lightning, IR and microwave observations; IV. Evaluate the error statistics of the scheme’s precipitation parameter retrievals. 2. Methodology 2.1 Microwave brightness temperature In the microwave wavelengths the radiation penetrates the clouds. Precipitating particles (water droplets and ice particles) interact strongly with the microwave radiation. Depending on the frequency, some of these particles emit, absorb, or scatter the radiation. Spencer et al (1989) have shown calculations of scattering and absorption by rain particles for three frequencies (19.35, 37 and 85.5 Ghz). They concluded that ice particles do not absorb microwave radiation, but only scatters; liquid water droplets mainly absorb; and finally scattering and absorption both increase with frequency and rainfall intensity. The disadvantage of using these sensors for rainfall retrieval arises from the fact that they have low field of view resolution (15-50 km) and poor sampling frequency (~twice a day). Over land rainfall estimation algorithms are primarily dependent on the scattering channels (85 Ghz), while over ocean more reliable multi-frequency techniques are used. 3724 The ice particles are present in intense and developed clouds and have a strong signal at the higher frequencies in microwave radiation. Grody (1991) has developed a scattering index for identifying varying scattering surfaces. This index uses a combination of the brightness temperature at 19, 22, 37 and 85 Ghz vertically polarized signal. Such methodology can be applied to define regions with ice particles. The knowledge of ice content can improve the computation of latent heat release responsible for phase changes in the cloud system. Information on latent heat release can be used to initialize numerical weather prediction models. In seeking a procedure to relate ice content with electrical discharges, lightning measurements from NLDN and brightness temperature from TRMM-TMI were coupled for a set of five events during December 1997 and January 1998. The mean number of flashes per 1x1 degree boxes conditional to the TRMM brightness temperature is shown in figure 1, in a manner similar to what was shown by Mohr et al.(1996). It is apparent that the number of flashes per area is inversely proportional to the brightness temperature. Figure 1. Mean number of flashes per 1x1 degree categorized by the brightness temperature at 85 Ghz. We propose to develop climatological (based on several months of data) relationships between lightning and 85 Ghz brightness temperature. Subsequently, one may use existing parametrizations of microwave to ice content. Such parametrization may be derived from physical retrievals based on radiative transfer model simulations and satellite observations. In this study the variational method of Grecu and Anagnostou (2000), which combines efficiently satellite radar and radiometer observations based on an optimization method, will be used to derive the necessary parametrizations. Figure 2 presents an example of the method’s retrieved vertical integrated ice content versus the corresponding observed 85 Ghz brightness temperatures for several storm cases over land. 3725 Figure 2. Vertical integrated ice content derived from Grecu and Anagnostou (2000) variational method versus corresponding 85 Ghz microwave brightness temperature observations. 2.2 Radar Reflectivity Factor Radar rainfall rate, R(mmh-1), is commonly retrieved by a known Z-R relationship. The Z-R relationship is evaluated based on ground measurements such as rain gauges and/or disdrometers (drop size distribution counters). Battan (1973) has compiled several Z-R relationships for different types of precipitation systems around the world. In the absence of validation measurements, the Marshal and Palmer (1948) exponential drop size distribution is applied. Therefore, the radar rainfall estimation suffers from a host of uncertainties. Nevertheless, weather radars are the only instruments capable of continuously monitoring precipitation at high spatio-temporal scales and over large areas 400-km by 400-km. It is expected that with a proper rain gauge calibration radar rainfall algorithm may provide reliable rainfall field estimates (Anagnostou and Krajewski, 1998). Statistical analyses that combines the TRMM precipitation radar and lightning measurements over larger regions over the globe, can lead to better understanding of the different types of precipitation systems. These results can be applied in conjunction with other remote sensed measurements to improve the rainfall estimation. Example of such statistical analysis is evaluation of the probability density functions of reflectivity versus height for varying lightning density categories. Figure 3 presents the vertical frequency distribution of reflectivity factor on the presence of lightning strikes from NLDN for the storms used in figure 1. The mean vertical profile is plotted as a thick black line. It is noteworthy that the mean vertical profile of radar reflectivity resembles the profiles of convective storm types. In addition, high values are observed near the surface, which means high precipitation, and a second peak near the freezing level. 3726 Figure 3. Vertical frequency distribution of TRMM-PR reflectivity values conditional to lightning occurrence. The line represents the mean vertical radar reflectivity profile. 2.3 Development of a Multi-Remote Sensor Rain Parameters retrival: Combination of lightning, IR, MW and PR measurements. The algorithm proposed herein applies the lightning location error correction scheme and the statistical relationships developed in the analysis described in the previous section. These relationships would use the continuous measurements of lightning and infra-red GOES images to retrieve instantaneous convective and stratiform rainfall fields at 0.5x0.5 degree grid areas. The area resolution is selected to minimize uncertainty associated with lightning location errors (10-50 km depending on the range). The temporal scale of agreegation is 30 minutes. This scale corresponds to the time interval of available GOES8 images. The above relationships and multi-sensor algorithm are assessed based on coincident TRMM-Sferics measurements for the period December, 1997 to January, 1998. The proposed multicomponent algorithm is summarized below. A cloud classification, which combines lightning and infra-red brightness temperature observations, is used to determine the type of precipitation system: electric-active (convective) and clouds with no lightning (stratiform). An IR temperature threshold to delineate rainy versus non-rainy areas will be obtained by matching microwave-based rain estimates (GPROF algorithm) with IR observations for both types of systems. Databases of matched probability density functions (pdf) between lighting frequency and surface rainfall rate (derived from GPROF), radar reflectivity factor, and ice content, will be evaluated for the two precipitation types, as a function of geographic location, time, land/ocean background, and cloud’s infra-red brightness temperature. The rainfall rate pdf is retrieved from the assumption that the Goddard Profiling alogorithm (GPROF), Kummerow and Giglio (1994a, 1994b), represents reliably the precipitation fields. The ice content pdf will be retrieved from relationship obtained from Grecu and Anagnostou (2000) variational method applied to coincident PR/TMI and lightning 3727 data. Finally the radar reflectivity factor will be obtained by statistical analyses of the vertical profiles of TRMM-PR in the presence of lightning. It is expected that the derived climatological relationships would have significant uncertainty if applied to cloud system with meteorological characteristics different to the ones studied. Subsequently, an assimilation scheme is proposed, where whenever a new TRMM overpass is over an area of interest brightness temperature and radar reflectivity data from TRMM are augmented with lightning and infra-red temperature observations and new pdfs are compared with the current database to establish the relationships that better represent the type of systems observed. 2.4 Rain Parameters retrieval errors The month of February 1998 will be used for algorithm assessment. The rain parameter products will be evaluated at the instantaneous, hourly, and daily time scales. Independent data sets will be used for these comparisons. A statistical analysis of the errors will produce the expected mean errors and variances for the rain parameters estimates. Rainfall estimates will be compared with rain gauges and satellite estimates from GPI and GPROF. The rain gauges are located in Florida over the coverage area of Melbourne weather radar. Hourly to daily comparisons will be performed. Instantaneous comparisons over the entire month of February will be carried for GPI and GPROF in the region of 130W60W and 50N-10S. The radar reflectivity factor errors will be estimated from the precipitation radar of TRMM and the Melbourne (FL) weather surveillance radar (WSR-88D). Constant altitude plan position indicator (CAPPI) reflectivity values above the freezing level will be used for the comparisons. Ice content retrievals will be tested against the retrievals obtained from Grecu and Anagnostou (2000) variational method for all the TRMM overpasses over the period selected for comparisons against GPROF. 3. Conclusion The proposed paper outlined a methodology for rain parameter retrieval from continuous measurements of lightning combined with infra-red GOES8 images and TRMM precipitation radar and microwave brightness temperature. It is strengthened by the fact that lightning is related to the convective regions of precipitation where ice is present. The optimal configuration of the technique is based on statistical (empirical) relationships between matched remote sensing measurements. The scheme involves a lightning location error correction scheme, which is incorporated to minimize uncertainties associated with locating correctly the convective cores. The proposed multi-component remote sensing rainfall estimation algorithm is an attractive tool for the continuous monitoring of precipitation over remote and un-gauged regions of earth. 4. References Anagnostou, E.N., and W.F. Krajewski, 1998: Calibration of the WSR-88D Precipitation Processing Subsystem. Weather and Forecasting, 13, 396-406. Baker, M.B., H.J. Christian, J. Latham, 1995: A computational study of the relationships linking lightning frequency and other thundercloud parameters, Q. J. R. Meteorol. Soc., 121, pp. 15251548. 3728 Battan, L.J., 1973: Radar Observation of the Atmosphere. pp. 324, The University of Chicaco Press. Buechler, D., H.J. Chirstian, S.J. Goodman, 1994: Rainfall estimation using lightning data. Seventh Conf. Satell. Meteor. and Ocean., Amer. Meteorol. Soc., June 6-10, pp. 171-174. Cheze, J.L. and H. Sauvaguot, 1997: Area average rainfall and lightning activity., J. Geophys. Res., 102(D2), pp. 1707-1715. 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