Rain Gauge Network Data Research Articles

Statistical evaluation of satellite-based CHIRPS precipitation data averaged over the midland and highland regions of Kidangoor sub-catchment, Kerala

The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.

Estimation of Rainfall via IMERG-FR and Its Relationship with the Records of a Rain Gauge Network with Spatio-Temporal Variation, Case of Study: Mexican Semi-Arid Region

In the last few years, Satellite Precipitation Estimates (SPE) have been increasingly used for rainfall estimation applications. Their validity and accuracy are influenced by several factors related to the location where the SPEs are applied. The objective of this study is to evaluate the performance of the Integrated Multisatellite Retrievals for Global Precipitation Measurement Version 06 Half-Hour Temporal Resolution (IMERG-FR V06 HH) for rainfall estimation, as well as to determine its relationships with the hourly and daily rain gauge network data in a semiarid region during 2019–2021. The methodology contemplates the temporality, elevation, rainfall intensity, and rain gauge density variables, carrying out a point-to-pixel analysis using continuous, (Bias, r, ME, and RMSE), categorical (POD, FAR, and CSI), and volumetric (VHI, VFAR, and VCSI) statistical metrics to understand the different behaviors between the rain gauge and IMERG-FR V06 HH data. IMERG-FR greatly underestimated the heavy rainfall events in values of −63.54 to −23.58 mm/day and −25.29 to −11.74 mm/30 min; however, it overestimates the frequency of moderate rain events (1 to 25 mm/day). At making the correlation (r) between the temporal scales, the monthly temporal resolution was the one that better relates the measured and estimated data, as well as reported r values of 0.83 and 0.85, where records at shorter durations in IMERG-FR do not detect them. The weakness of this system, according to the literature and confirmed by the research findings, in the case of hydrological phenomena, is that recording or estimating short durations is essential for the water project, and therefore, the placement of rain gauges. The 1902–2101 m.a.s.l. range elevation has the best behavior between the data with the lowest error and best detection ability, of which IMERG-FR tended to overestimate the rain at higher altitudes. Considering that the r for two automated rain gauges per IMERG-FR pixel density was 0.74, this indicates that the automated rain gauges versus IMERG-FR have a better data fit than the rain gauges versus IMERG-FR. The distance to centroid and climatic evaluations did not show distinctive differences in the performance of IMERG. These findings are useful to improve the IMERG-FR algorithms, guide users about its performance at semiarid plateau regions, and assist in the recording of data for hydrological projects.

Daily rainfall assimilation based on satellite and weather radar precipitation products along with rain gauge networks

Abstract The analysis of the spatial and temporal distribution of storm events contributes to a better use of water resources, for example, the supply of drinking water, irrigation practices, electricity generation and management of extreme events to control floods and mitigate droughts, among others. The traditional observation of rainfall fields in Mexico has been carried out using rain gauge network data, but their spatial representativeness is unsatisfactory. Therefore, this study reviewed the possibility of obtaining better estimates of the spatial distribution of daily rainfall considering information from three different databases, which include rain gauge measurements and remotely sensed precipitation products of satellite systems and weather radars. In order to determine a two-dimensional rainfall distribution, the information has been merged with a sequential data assimilation scheme up to the diagnostic stage, paying attention to the benefit that the rain gauge network density has on the estimation. With the application of the Barnes method, historical events in the Mexican territory were analyzed using statistical parameters for the validation of the estimates, with satisfactory results because the assimilated rainfalls turned out to be better approximations than the values calculated with the individual databases, even for a not very low density of surface observations.

Mapping Australia’s precipitation: harnessing the synergies of multi-satellite remote sensing and gauge network data

ABSTRACT Coverage, resolution, and accuracy in the spatial and temporal estimates of remotely sensed precipitation from space satellites, along with the number of instruments deployed to deliver these observations, are increasing. Of key interest in this study is the unsurpassed opportunity offered by their broad and continuous coverage to complement sparse, but more accurate, in situ rain gauge measurements for building climate resilience at the local, regional, and global scales. For many parts of the globe, this opportunity remains untapped. Australia is no exception and provides a unique challenge, given the small fraction of the continent that has rain gauges, the highly diverse climate due to its large size, and the apparent worsening of extreme weather events in both frequency and intensity. Notwithstanding this great impetus, a continent-wide record of multi-satellite-gauge fused precipitation data for Australia remains lacking. This missing data asset is a prerequisite for understanding the emerging spatiotemporal dynamics of precipitation, without which reliable forecasts would be difficult if not impossible. This study seeks to address this need. Here, we develop a method which can synergistically fuse precipitation data from different sources. More specifically, the aim of this study is to develop Precipitation Profiler-Observation Fusion and Estimation (PPrOFusE), a tool to deliver high-quality gauge and multi-satellite fused precipitation data. We test and apply this tool for Australia, but it is by no means limited in scope to this region. By design, PPrOFusE has a built-in capability to assess the strengths and weaknesses of each platform. In this case study, we fused data for a period of 22 years (2000–2022) using the rain gauge network data from the Australian Bureau of Meteorology (BOM) together with satellite data from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Center Morphing technique (CMORPH). Our proposed precipitation data fusion method consists of two steps. Step 1, the relationship among the three sources of data is modeled by multiple linear regression at each rain gauge location, returning the least squares estimates for the associated regression coefficient vector. Step 2, such regression coefficient vector estimates for all rain gauge locations are fitted by a spatial autoregression model, whereafter the multiple linear regression coefficient vectors for those locations void of rain gauges are predicted by spatial interpolation. Key findings are twofold. First, CMORPH is more accurate for most regions of Australia than GSMaP. Second, a clustering analysis of the fused precipitation over the last 20 years suggests two key trends on Australia’s changing climate, relative to BOM’s six major climate zones, from the previous century: (a) increased spatial variability to the north, consistent with meteorological expectations, amid a southwards expansion of the wet summer dominant zones across the continent; (b) the edge of the arid region shifts southwards and pushes out Mediterranean climate and winter dominant rainfall zones across southern Australia.

Improving GSMaP V06 precipitation products over the Upper Tocantins River basin in the Brazilian Cerrado, based on local rain-gauge network

Gridded precipitation products from remote sensing are currently available and could potentially enhance the use of precipitation data in regions with sparse network of ground rain gauges. Thus, this study aimed to evaluate the performance and propose correction models for Global Satellite Mapping of Precipitation (GSMaP) use in the Upper Tocantins River basin, a key area for food and electricity production in central Brazil, but with sparse network of ground rain gauges, challenging water resources and agriculture decision makers. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1 ~ 1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October–April) than in the dry season (May–September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area. The findings of this study indicate how decision makers could adjust and apply GSMaP products to estimate rainfall for water resources, agriculture and drought management challenges in the Upper Tocantins River basin.

Aquappolis: Mobile application for rainwater harvesting in Mexico City

Rainwater harvesting has become a necessary action around the world to satisfy the big cities needs where the water access is restrained (Shivakumar, 2017), which is the case of Mexico City where the water demand exceeds the supply sources, and many other problems are related to water (Arto-Olaizola et al., 2016). Aquappolis is a mobile application intended to compute the amount of rainwater that can be harvested from specific zones in Mexico City. The app information is provided from extensive rain gauge network data collected from Hydrological Observatory stations across Mexico City. This paper provides an example of how the technological innovation and instant information available for decision maker authorities and general population are indispensable to achieve a responsible, careful, and sustainable management of available water resources in Mexico´s Valley.

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.

Performances of GPM satellite precipitation over the two major Mediterranean islands

This study aims to assess the reliability of satellite-precipitation products from the Global Precipitation Measurements (GPM) mission in regions with complex landscape morphology. Our analysis is carried out in the European mid-latitude area, namely on the two major islands of Mediterranean Sea, i.e. Sardinia and Sicily (Italy). Both islands experience precipitation originating from the interaction of steep orography on the coasts with winds carrying humid air masses from the Mediterranean Sea. The GPM post real-time IMERG (Integrated Multi-satellitE Retrievals from Global Precipitation Measurement) “Final” run product at 0.1° spatial resolution and half-hour temporal resolution have been selected for the two-year 2015–2016 period. Evaluation and comparison of the selected product, with reference to raingauge network data, are performed at hourly and daily time scales using statistical and graphical tools. The influences of morphology and land-sea coastal area transition on the reliability of the GPM product have been analysed.Confirming previous studies, results showed that GPM satellite data slightly overestimate rainfall over the study areas, but they are well correlated with the interpolated raingauge data. Metrics based on occurrences above a given threshold and on total volume above the same threshold were applied and revealed better performances for the latter ones. Applying the same metrics we show how GPM performances improve as the temporal aggregation increases. Several drawbacks were detected in the coastal areas, which were characterized by worse performances than internal areas. Statistics are generally very similar for the two considered case studies (i.e., Sardinia and Sicily) except for correlation between topography and accuracy of GPM products, which was slightly higher for Sardinia.

More frequent intense and long-lived storms dominate the springtime trend in central US rainfall.

The changes in extreme rainfall associated with a warming climate have drawn significant attention in recent years. Mounting evidence shows that sub-daily convective rainfall extremes are increasing faster than the rate of change in the atmospheric precipitable water capacity with a warming climate. However, the response of extreme precipitation depends on the type of storm supported by the meteorological environment. Here using long-term satellite, surface radar and rain-gauge network data and atmospheric reanalyses, we show that the observed increases in springtime total and extreme rainfall in the central United States are dominated by mesoscale convective systems (MCSs), the largest type of convective storm, with increased frequency and intensity of long-lasting MCSs. A strengthening of the southerly low-level jet and its associated moisture transport in the Central/Northern Great Plains, in the overall climatology and particularly on days with long-lasting MCSs, accounts for the changes in the precipitation produced by these storms.

Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea

Precipitation is one of the major variables for many applications and disciplines related to water resources and the geophysical Earth system. Satellite retrieval systems, rain-gauge networks, and radar systems are complementary to each other in terms of their coverage and capability of monitoring precipitation. Satellite-rainfall estimate systems produce data with global coverage that can provide information in areas for which data from other sources are unavailable. Without referring to ground measurements, satellite-based estimates can be biased and, although some gauge-adjusted satellite-precipitation products have been already developed, an effective way of integrating multi-sources of precipitation information is still a challenge.In this study, a specific area, the Sicilia Island (Italy), has been selected for the evaluation of satellite-precipitation products based on rain-gauge data. This island is located in the Mediterranean Sea, with a particular climatology and morphology, which can be considered an interesting test site for satellite-precipitation products in the European mid-latitude area. Four satellite products (CMORPH, PERSIANN, PERSIANN-CCS, and TMPA-RT) and two GPCP-adjusted products (TMPA and PERSIANN Adjusted) have been selected. Evaluation and comparison of selected products is performed with reference to data provided by the rain-gauge network of the Island Sicilia and by using statistical and graphical tools. Particular attention is paid to bias issues shown both by only-satellite and adjusted products. In order to investigate the current and potential possibilities of improving estimates by means of adjustment procedures using GPCC ground precipitation, the data have been retrieved separately and compared directly with the reference rain-gauge network data set of the study area.Results show that bias is still considerable for all satellite products, then some considerations about larger area climatology, PMW-retrieval algorithms, and GPCC data are discussed to address this issue, along with the spatial and seasonal characterization of results.

Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms

Abstract. Convective clouds generate extreme rainfall events and flash floods in small areas with both large spatial and temporal variability. For this reason, the monitoring of the total accumulated precipitation fields at the surface with rain gauges and meteorological radars has both strengths and weakness. Alternatively, a numerical cloud model may be a useful tool to simulate convective precipitation for various analyses and predictions. The main objective of this paper is to show that the cloud-resolving model reproduces well the accumulated convective precipitation obtained from the rain gauge network data in the area with frequent split storms. We perform comparisons between observations and model samples of the areal accumulated convective precipitation for a 15-year period over treated area. Twenty-seven convective events have been selected. Statistical analyses reveal that the model areal accumulated convective precipitation closely match their observed values with a correlation coefficient of 0.80.

Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications

Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

다방향 흐름 분배와 실시간 보정 알고리듬을 이용한 분포형 강우-유출 모형 개발(II) - 적용 -

본 연구에서는 다방향 흐름 분배 알고리듬과 실시간 유출 보정 알고리듬을 이용하여 개발한 분포형 강우 유출모형의 실제 유역에 대한 적용성을 평가하였다. 안동댐과 남강댐 유역에 대해 각각 우량계 관측자료와 레이더 관측 우량자료를 이용하여 분포형 강우-유출 모의를 수행하였다. 이전의 호우사상에 대한 유역 매개변수 보정 실시 후 보정된 매개변수를 이용하여 다른 호우사상에 대해 모형을 적용하였다. 안동댐 유역에 대해 흐름 군집화 결과를 제시함으로써 다방향 흐름 분배 알고리듬의 흐름추적에 대한 물리적 타당성을 입증하였다. 다방향 흐름 분배 알고리듬을 이용하여 정확도를 유지하면서 계산소요시간 단축이 이루어졌으며 실시간 유출 보정 알고리듬의 적용을 통해 좀 더 향상된 정확도를 갖는 결과가 도출되었다. 본 연구에서 개선된 유역 홍수관리를 위한 분포형 강우 유출 모형의 활용가능성을 제시하였다. The applicability of the developed distributed rainfall runoff model using a multi-directional flow allocation algorithm and a real-time updating algorithm was evaluated. The rainfall runoff processes were simulated for the events of the Andong dam basin and the Namgang dam basin using raingauge network data and weather radar rainfall data, respectively. Model parameters of the basins were estimated using previous storm event then those parameters were applied to a current storm event. The physical propriety of the multi-directional flow allocation algorithm for flow routing was validated by presenting the result of flow grouping for the Andong dam basin. Results demonstrated that the developed model has efficiency of simulation time with maintaining accuracy by applying the multi-directional flow allocation algorithm and it can obtain more accurate results by applying the real-time updating algorithm. In this study, we demonstrated the applicability of a distributed rainfall runoff model for the advanced basin-wide flood management.

Distributed hydrological modelling using weather radar in gauged and ungauged basins

Distributed hydrological modelling using space–time estimates of rainfall from weather radar provides a natural approach to area-wide flood forecasting and warning at any location, whether gauged or ungauged. However, radar estimates of rainfall may lack consistent, quantitative accuracy. Also, the formulation of hydrological models in distributed form may be problematic due to process complexity and scaling issues. Here, the aim is to first explore ways of improving radar rainfall accuracy through combination with raingauge network data via integrated multiquadric methods. When the resulting gridded rainfall estimates are employed as input to hydrological models, the simulated river flows show marked improvements when compared to using radar data alone. Secondly, simple forms of physical–conceptual distributed hydrological model are considered, capable of exploiting spatial datasets on topography and, where necessary, land-cover, soil and geology properties. The simplest Grid-to-Grid model uses only digital terrain data to delineate flow pathways and to control runoff production, the latter by invoking a probability-distributed relation linking terrain slope to soil absorption capacity. Model performance is assessed over nested river basins in northwest England, employing a lumped model as a reference. When the distributed model is used with the gridded radar-based rainfall estimators, it shows particular benefits for forecasting at ungauged locations.

Daily Stream Flow Prediction Capability of Artificial Neural Networks as influenced by Minimum Air Temperature Data

The minimum air temperature influence on training of artificial neural networks (ANNs) for daily stream flow prediction is investigated. For improved computed performances, efficiencies of the conjugate gradient (CG) and Levenberg–Marquardt (L–M) training algorithms are compared. The minimum air temperature influence is studied for a watershed in southern Iran, representing a continuous flow regime with peak flows occurring as a result of individual rainfall events. Peak flow occurrence generally coincides with freezing or near-freezing periods. Based on the results, the L–M algorithm is more efficient than the CG algorithm, so it is used to train four ANNs models for stream flow prediction at time step t+1 from time step t input. Model 1 uses enabled stream flow data as input dimension, Model 2 uses enabled stream flow and single raingauge, Model 3 uses enabled raingauge network and Model 4 uses enabled raingauge network data and minimum air temperature. Validation stage root mean square error (RMSE), root mean absolute error (RMAE) and efficiency (EF) measures are: 25·456, 0·220, 0·580 (Model 1); 17·401, 0·139, 0·813 (Model 2); 12·220, 0·112, 0·916 (Model 3) and 10·383, 0·069, 0·939 (Model 4), respectively. The influence of minimum air temperature on improved Model 4 performance is discussed.

Runoff simulation using radar and rain gauge data

The conceptual rainfall-runoff model TOPMODEL is used to simulate runoffs of the Meishan and Nianyushan catchments during the summers of 1998 and 1999 in the GAME/HUBEX (GEWEX Asia Monsoon Experiment/HUAIHE River Basin Experiment) project. The rainfall distributions are estimated by weather radar and rain gauge networks according to different methods. Observed and simulated runoffs are compared and analyzed for both catchments. Results show that (1) the runoff of the catchment is best simulated by radar data combined with rain gauge network data from inside the catchment, and (2) the rainfall estimated by radar adjusted by a few rain gauges outside the catchment can be used to simulate runoff equally as well as using the dense rain gauge network alone.

A GIS methodology for the analysis of weather radar precipitation data

A GIS multi-component module was developed within the ArcView GIS environment for processing and analysing weather radar precipitation data. The module is capable of: (a) reading geo-reference radar data and comparing it with rain-gauge network data, (b) estimating the kinematics of rainfall patterns, such as the storm speed and direction, and (c) accumulating radar-derived rainfall depths. By bringing the spatial capabilities of GIS to bear this module can accurately locate rainfall on the ground and can overlay the animated storm on different geographical features of the study area, making the exploration of the storm's kinematic characteristics obtained from radar data relatively simple. A case study in the City of Hamilton in Ontario, Canada is used to demonstrate the functionality of the module. Radar comparison with rain gauge data revealed an underestimation of the classical Marshal & Palmer Z–R relation to rainfall rate.

Satellite measurements supplemented with meteorological data to operationally estimate evaporation in Sri Lanka

Ten day estimates of actual evaporation in Sri Lanka have been made for the period June 1999–2000, using AVHRR radiances from day-time ascending mode passes of the NOAA 14 meteorological satellite, supplemented with meteorological data measured simultaneously at ground level. During the 12-month period of the study, a total of 88 images have been processed using a residual energy budget and temporally constant evaporative fraction approach to yield 36 estimates of 10-day evaporation for the entire land area of Sri Lanka at 1 km 2 resolution. The evaporation estimates, when compared with data from a large aperture scintillometer, showed errors of 17 and 1% in the case of 10-day and monthly evaporation respectively. The comparison with available independent evaporation estimates for specific catchments showed errors ranging from 2 to 8%. These estimates together with images of soil moisture status estimated using remote sensing techniques and rainfall from rain gauge network data were incorporated in a geographic information system (GIS). This provides a solid basis for understanding water consumed by agriculture and environment, in addition to providing a solid foundation for understanding the efficiency of major irrigation systems. The availability of data for the whole country and in a GIS environment with the facility for conducting studies at various spatial scales increases their usefulness for hydrological research in Sri Lanka.

The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dBZ) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).

Validation of retrieval algorithms for the infrared remote sensing of precipitation with the Sardinian rain gauge network data

A few retrieval techniques for the estimate of precipitation from infrared satellite data, already tested over the tropical Atlantic Ocean and over continental mid-latitude regions, have been used to verify their performance for the study of the western Mediterranean Sea climate. An attempt to optimize these techniques has been made, and a detailed comparison of the remotely sensed precipitation within situ continuous data, obtained with the rain gauge network in Sardinia for two significant periods of 1988, is presented. The results obtained give some useful indications on the performance of these techniques, on the best area time resolution to be used for their validation, and on the possible applications of the retrieved information.