Rainfall screening methodology using TRMM data over a river basin

ABSTRACT

A regionalized rain/no-rain classification (RNC) based on scattering index methodology is developed for detecting rainfall signatures over the land regions of the Mahanadi basin (India), using data products from the passive and active sensors onboard the Tropical Rainfall Measuring Mission (TRMM), namely the TRMM Microwave Imager (TMI) and Precipitation Radar (PR). The proposed model, developed using data for two years from the orbital database, was validated using PR and in-situ data for selected case study events in 2011 and 2012. Performance evaluation of the model is discussed using 10 metrics derived from the contingency table. Overall, the results show superior performance, with an average probability of detection of 0.83, bias of 1.10 and odds ratio skill score greater than 0.93. Accurate rainfall detection is obtained for 95% of case study events. The relative performance of the proposed model is dependent on rainfall type, but it should be useful in rainfall retrieval algorithms for current missions such as the Global Precipitation Measurement Mission.

Editor M.C. Acreman; Associate editor Y. Gyasi-Agyei     

Use of Scale Recursive Estimation for assimilation of precipitation data from TRMM (PR and TMI) and NEXRAD

The paper shows an application of Scale Recursive Estimation (SRE) used to assimilate rainfall rates estimated during a storm event from three remote sensing devices. These are the TMI radiometer and the PR radar, carried on board of the TRMM satellite and the KNQA Memphis Weather Surveillance radar, belonging to the NEXRAD network, each one providing rain rate estimates at a different spatial scale. The variability of rain rate process in scales is modeled as a multiplicative random cascade, including spatial intermittence. The observational noise in the estimates is modeled according to a multiplicative error. System estimation, including process and observational noise, is carried out using Maximum Likelihood Estimation implemented by a scale recursive Expectation Maximization (EM) algorithm. As a result, new rainfall rate estimates are obtained that feature decreased estimation error as compared to those coming from each device alone. The performance of the SRE-EM approach is compared with that of the latest methods proposed for data fusion of multisensor estimates. The proposed approach improves the current methods adopted for SRE and provides an alternative for data fusion in the field of precipitation.     

Lightning activity and precipitation structure of hailstorms

By using the cloud-to-ground (CG) lightning location data from the lightning detection network of He- nan Province, surface Doppler radar data and standard orbit data of PR, TMI and LIS on TRMM satellite, the spatial and temporal characteristics of CG lightning flashes in 10 severe hailstorms are analyzed. The results show that the percentage of CG lightning in these hailstorms is high with an average value of 45.5%. There is a distinct increase in CG flash rate during the rapid development stage of hailstorms. The hailstone falling corresponds to an active positive flash period, and the increase of CG flash rate is generally accompanied with a decrease of –CG flash rate. The flash rate declines rapidly during the dissipating stage of hailstorms. The precipitation structure and lightning activity in two typical hail- storms are studied in detail. It is found that strong convective cells with reflectivity greater than 30dBZ mainly are situated in the front region of hailstorms, whereas the trailing stratiform region is in the rear part of the hailstorms. The maximum heights of echo top are higher than 14 km. Convective rain con- tributes much more rainfall to the total than stratiform rain, and the convective rain takes about 85% and 97% of the total in the two cases, respectively. Total lightning in the hailstorms is very active with the flash rate up to 183 fl/min and 55 fl/min, respectively. The results also indicate that most lightning flashes occurred in the echo region greater than 30 dBZ and its immediate periphery. The probability of lightning occurrence is 20 times higher in the convective region than in the stratiform region. The result suggests that the lightning information is helpful to the identification of convective rain region. The linear relationship between flash rate and ice water content is disclosed primarily.     

The influence of rainfall on PR radar measurement of ocean surface wind speed and its calibration

Rain can significantly degrade the wind vector retrieval from Precipitation Radar(PR) by three mechanisms, namely, two-way rain attenuation, rain volume-backscattering, and ocean surface roughening from the rain splash effect. Here we first derive the radar equation for PR in rainy conditions. Then we use the rain attenuation model for Ku band, volume backscatter model for spherical raindrops and PR–TMI(TRMM Microwave Imager, TMI) matchup datasets from June to August in 2010 to solve the radar equation, and quantitatively analyze the influence of rainfall on PR radar measurement of ocean surface wind speed. Our results show that the significant effect of rain on radar signal is dominated by two-way rain attenuation and rain splash effect, and the effect of rain volume-backscattering is relatively the weakest, which can even be neglected in rain-weak conditions. Moreover, both the two-way rain attenuation and rain splash effect increase with the increasing of integration rain rate and incident angle. Last, we combine volume-backscattering effect and splash effect into a simple phenomenological model for rain calibration and select three typhoon cases from June to August in 2012 to verify the accuracy of this model. Before calibration, the mean difference and mean square error(MSE) between PR-observed ? 0 and wind-induced ? 0 are about 2.95 dB and 3.10 dB respectively. However, after calibration, the mean difference and MSE are reduced to 0.64 dB and 1.61 dB respectively. The model yields an accurate calibration for PR near-nadir normalized radar cross section(NRCS) in rainy conditions.     

Estimation of rainfall from infrared‐microwave satellite data for basin‐scale hydrologic modelling

The infrared‐microwave rainfall algorithm (IMRA) was developed for retrieving spatial rainfall from infrared (IR) brightness temperatures (TBs) of satellite sensors to provide supplementary information to the rainfall field, and to decrease the traditional dependency on limited rain gauge data that are point measurements. In IMRA, a SLOPE technique (ST) was developed for discriminating rain/no‐rain pixels through IR image cloud‐top temperature gradient, and 243K as the IR threshold temperature for minimum detectable rainfall rate. IMRA also allows for the adjustment of rainfall derived from IR‐TB using microwave (MW) TBs. In this study, IMRA rainfall estimates were assessed on hourly and daily basis for different spatial scales (4, 12, 20, and 100 km) using NCEP stage IV gauge‐adjusted radar rainfall data, and daily rain gauge data. IMRA was assessed in terms of the accuracy of the rainfall estimates and the basin streamflow simulated by the hydrologic model, Sacramento soil moisture accounting (SAC‐SMA), driven by the rainfall data. The results show that the ST option of IMRA gave accurate satellite rainfall estimates for both light and heavy rainfall systems while the Hessian technique only gave accurate estimates for the convective systems. At daily time step, there was no improvement in IR‐satellite rainfall estimates adjusted with MW TBs. The basin‐scale streamflow simulated by SAC‐SMA driven by satellite rainfall data was marginally better than when SAC‐SMA was driven by rain gauge data, and was similar to the case using radar data, reflecting the potential applications of satellite rainfall in basin‐scale hydrologic modelling. Copyright © 2010 John Wiley & Sons, Ltd.     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

One-minute rain rate distribution in Nigeria derived from TRMM satellite data

Data from the Tropical Rainfall Measuring Mission (TRMM) satellite sensors, the Microwave Imager (TMI, 3A12 V6) and other satellite sources (3B43 V6) have been used to derive the thunderstorm ratio β, total rain accumulation M, and 1-min rainfall rates, R1min, for 37 stations in Nigeria, for 0.001–1% of an average year, for the period 1998–2006. Results of the rain accumulations from the TRMM satellite (1998–2006) were compared with the data collected from 14 ground stations in Nigeria for the period 1991–2000. The two data sets are reasonably positively correlated, with correlation coefficients varying from 0.64 to 0.99. Deduced 1-min rainfall rates compared fairly well with the previous ground data of Ajayi and Ezekpo (1988. Development of climatic maps of rainfall rate and attenuation for microwave applications in Nigeria. The Nigerian Engineering 23(4), 13–30) with correlation coefficients varying from 0.17 to 0.97 in all 37 stations. The agreement was much better when compared with the International Telecommunications Union Radio communication Study group 3 digital maps with correlation coefficients varying from 0.84 to 0.98 in 23 locations; however there were negative correlation coefficient (of 0.2 in 7 stations) in the Middle Belt and a weak positive coefficient (of 0.09 in 6 stations) in the South South. Regionally the inferred mean annual 1-min rainfall rates are the highest in the South-East region with values between 111 and 125 mm/h throughout the 9 years, followed by the South-South region (105–124 mm/h). The lowest rainfall rate and rainfall accumulation occur in the North-West region (60–86 mm/h) followed, in ascending order, by the North-East (66–95 mm/h) region, the Middle-Belt region (76–102 mm/h) and the South-West region (77–110 mm/h). The present results were also compared with 9 tropical stations around the world and there was positive correlation between the results. The present results will be very useful for satellite rain attenuation modeling in the tropics and subtropical stations around the world.It is useful to note that one country, particularly one as large as Nigeria, can have significant variations in its rainfall characteristics for a variety of reasons, and this is borne out by the results presented.     

An examination of summer precipitation over Asia based on TRMM/TMI

A 6-year dataset of summer monthly mean precipitation derived from Tropical Precipitation Measure-ment Mission (TRMM)-Microwave Imager (TMI) was used to delineate the spatial distribution patterns of precipitation throughout Asian areas, which indicates that there are three rainfall centers located at the northern coast of the Bay of Bengal, the South China Sea and the western equatorial Pacific Warm Pool, respectively. Based upon the analysis of horizontal distribution, the capability of TMI for characterizing terrestrial and maritime precipitation has been evaluated and compared with Global Precipitation Climatology Project (GPCP) dataset. It was found that TMI and GPCP are well consistent with each other, while a few significant differences occur at several regions over land. By investigating rainfall esti-mates over six specific locations in Asia, a systematic underestimation of TMI was demonstrated, which could be explained by the inherent deficiency within TMI terrestrial algorithm relying on scat-tering signal from ice particles in a precipitation system. A further analysis shows that the highly in-homogeneous distribution of rain gauges employed by GPCP contributes a great deal to the significant discrepancy between GPCP and TMI, especially over regions surrounding the Tibetan Plateau where rain gauges are quite scarce.     

Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

The accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point‐based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.     

An improved genetic programming to SSM/I estimation typhoon precipitation over ocean

This article proposes an improved multi‐run genetic programming (GP) and applies it to estimate the typhoon rainfall over ocean using multi‐variable meteorological satellite data. GP is a well‐known evolutionary programming and data mining method used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. However, the searching efficiency of traditional GP can be decreased by the complex structure of parse tree to represent the multiple input variables. This study processed an improvement to enhance escape ability from local optimums during the optimization procedure. We continuously run GP several times by replacing the terminal nodes at the next run with the best solution at the current run. The current method improves GP, obtaining a highly nonlinear meaningful equation to estimate the rainfall. In the case study, this improved GP (IGP) described above combined with special sensor microwave imager (SSM/I) seven channels was employed. These results are then verified with the data from four offshore rainfall stations located on islands around Taiwan. The results show that the IGP generates sophisticated and accurate multi‐variable equation through two runs. The performance of IGP outperforms the traditional multiple linear regression, back‐propagated network (BPN) and three empirical equations. Because the extremely high values of precipitation rate are quite few and the number of zero values (no rain) is very large, the underestimations of heavy rainfall are obvious. A simple genetic algorithm was therefore used to search for the optimal threshold value of SSM/I channels, detecting the data of no rain. The IGP with two runs, used to construct an appropriate mathematical function to estimate the precipitation, can obtain more favourable results from estimating extremely high values. Copyright © 2011 John Wiley & Sons, Ltd.     

Correcting the TRMM rainfall product for hydrological modelling in sparsely-gauged mountainous basins

This study developed a correction approach to improve the rainfall field estimation using the TRMM rainfall product in a sparsely-gauged mountainous basin. First, Thiessen polygons were generated to define the measurement domain of each raingauge. Second, the rainfall of TRMM pixels in each Thiessen polygon was corrected using a benchmark method based on the difference between the monthly rainfall estimated by a raingauge and the TRMM pixel that possessed a gauge station (referred to as a gauged pixel). Third, the rainfall values in the gauged pixels were adjusted to the weighted average value of the gauge rainfall and corrected pixel rainfall. Finally, the rainfall in the non-gauged TRMM pixels was corrected as the sum of two terms. The first term is the adjusted rainfall in the corresponding gauged pixel in the same Thiessen polygon, and the second term is the rainfall (after benchmark correction) difference between the current pixel and the gauged pixel. Our results indicate that the corrected rainfall data outperforms the original TRMM product in the simulations of moderate and low flows and outperforms the sparse raingauges in the simulations of both peak and low flows.

EDITOR A. Castellarin; ASSOCIATE EDITOR S. Huang     

Validation of AVHRR and TMI-derived sea surface temperature in the northern South China Sea

Satellite-derived SSTs are validated in the northern South China Sea (NSCS) using in situ SSTs from the drifting buoys and well-calibrated sensors installed on Research/Vessel(R/V) Shiyan 3. The satellite SSTs are Advanced Very High Resolution Radiometer (AVHRR) daytime SST, AVHRR nighttime SST, Tropical rainfall Measuring Mission Microwave Imager (TMI) daytime SST and TMI nighttime SST. Availability of satellite SST, which is the ratio that the number of available satellite SST to the total ocean pixels in NSCS is calculated; annual average SST availabilities of AVHRR daytime SST, AVHRR nighttime SST, TMI daytime SST and TMI nighttime SST are 68.42%, 69.99%, 56.57% and 52.80%, respectively. Though the TMI SST availability is nearly constant throughout the year, the variations of the AVHRR SST availability are much larger because of seasonal variations of cloud cover in NSCS. Validation of the satellite-derived SSTs shows that bias±standard deviation (STD) of AVHRR SST is −0.43±0.76 and −0.33±0.79 °C for daytime and nighttime, respectively, and bias±STD of TMI SSTs is 0.07±1.11 and 0.00±0.97 °C for daytime and nighttime, respectively. It is clear that AVHRR SSTs have significant regional biases of about −0.4 °C against the drifting buoy SSTs. Differences between satellite-derived−in situ SSTs are investigated in terms of the diurnal SST cycle. When satellite-derived wind speeds decrease down below 6 m/s, the satellite SSTs become higher than the corresponding in situ SSTs, which means that the SST difference (satellite SST−Buoy SST) is positive. This wind-speed dependence of the SST difference is consistent with the previous results, which have mentioned that low wind speed coupled with clear sky conditions (high surface solar radiation) enhance the diurnal SST amplitude and the bulk-skin temperature difference.     

星载微波ERS-1和多通道SSM/I观测海洋的相关性和随机粗糙面复合模型的数值模拟

欧洲遥感卫星（ERS）和美国防卫气象卫星计划（DMSP）空对地微波遥感是当今研究全球大气地表微波散射辐射和反演地球物理与水文参数的主要数据来源．本文研究了ERS－1散射计和SSM／I多通道辐射计在中国海域观测到的后向散射和热辐射数据,论证了同一地区同一时间段内ERS主动散射计和SSM／I被动辐射计观测数据的相关性．用海域主、被动遥感数据的比较,阐述了主、被动联合多通道分析方法有利于监视和分析复杂地表和海面在时间和空间尺度上的变化．用带泡沫散射层的双尺度随机粗糙面的复合模型计算后向散射和热发射,用以数值模拟ERS和SSM／I数据．并讨论了后向散射与热辐射数值模拟结果的相关性,以及与星载微波遥感器实际观测结果的比较．     

Understanding plot‐scale hydrology of Lesser Himalayan watershed—A field study and HYDRUS‐2D modelling approach

Soil moisture dynamics have a significant effect on overland flow generation. Catchment aspect is one of the major controlling factors of overland flow and soil moisture behaviour. A few experimental studies have been carried out in the uneven topography of the Himalayas. This study presents plot‐scale experiments using portable rainfall simulator at an altitude of 1,230 m above mean sea level and modelling of overland flow using observed datasets. Two plots were selected in 2 different aspects of Aglar watershed of Lesser Himalaya; the agro‐forested (AF) plot was positioned at the north aspect whereas the degraded (DE) plot was located at the south aspect of the hillslope. HS flumes and rain gauges were installed to measure the runoff at the outlet of the plot and the rainfall depth during rainfall simulation experiments. Moreover, 10 soil moisture sensors were installed at upslope and downslope locations of both the plots at 5, 15, 25, 35, and 45 cm depth from ground level to capture the soil moisture dynamics. The tests were conducted at intensities of 79.8 and 75 mm/hr in AF plot and 82.2 and 72 mm/hr in the DE plot during Test 1 and Test 2, respectively. The observed data indicate the presence of reinfiltration process only in the AF plot. The high water holding capacity and the presence of reinfiltration process results in less runoff volume in the AF plot compared with the DE plot. The Hortonian overland flow mechanism was found to be the dominant overland flow mechanism as only a few layers of top soil get saturated during all of the rainfall–runoff experiments. The runoff, rainfall, and soil moisture data were subsequently used to calibrate the parameters of HYDRUS‐2D overland flow module to simulate the runoff hydrograph and soil moisture. The components of hydrograph were evaluated in terms of peak discharge, runoff volume and time of concentration, the results were found to be within the satisfactory range. The goodness of fit of simulated hydrographs were more than 0.85 and 0.95 for AF and DE plot, respectively. The model produced satisfactory simulation results of soil moisture for all of the rainfall–runoff experiments. The HYDRUS‐2D overland flow module was found promising to simulate the runoff hydrograph and soil moisture in plot‐scale research.     

Stochastic overland flows Part 1: Physics-based evolutionary probability distributions

A theoretical solution framework to the nonlinear stochastic partial differential equations (SPDE) of the kinematic wave and diffusion wave models of overland flows under stochastic inflows/outflows, stochastic surface roughness field and stochastic state of flows was obtained. This development was realized by means of an eigenfunction representation of the time-space overland flow depths, and by transforming the problem into the phase space. By using Van Kampen's lemma and the cumulant expansion theory of Kubo-Van Kampen-Fox, the deterministic partial differential equation (PDE) for the evolutionary probability density function (pdf) of overland flow depths was finally obtained. Once this deterministic PDE is solved for the time-varying pdf of overland flow depths, then the time-space varying pdf of overland flow depths can be obtained by a transformation given in the text. In this solution framework it is possible to incorporate the stochastic dynamic behavior of the parameters and of the forcing functions of the overland flow process. For example, not only the individual rainfall duration and fluctuating rain intensity characteristics but also the sequential behavior of rainfall patterns is incorporated into the evolutionary probability density function of overland flow depths.     

Stochastic overland flows

A theoretical solution framework to the nonlinear stochastic partial differential equations (SPDE) of the kinematic wave and diffusion wave models of overland flows under stochastic inflows/outflows, stochastic surface roughness field and stochastic state of flows was obtained. This development was realized by means of an eigenfunction representation of the time-space overland flow depths, and by transforming the problem into the phase space. By using Van Kampen's lemma and the cumulant expansion theory of Kubo-Van Kampen-Fox, the deterministic partial differential equation (PDE) for the evolutionary probability density function (pdf) of overland flow depths was finally obtained. Once this deterministic PDE is solved for the time-varying pdf of overland flow depths, then the time-space varying pdf of overland flow depths can be obtained by a transformation given in the text. In this solution framework it is possible to incorporate the stochastic dynamic behavior of the parameters and of the forcing functions of the overland flow process. For example, not only the individual rainfall duration and fluctuating rain intensity characteristics but also the sequential behavior of rainfall patterns is incorporated into the evolutionary probability density function of overland flow depths.     

Evaluation of remote‐sensing‐based rainfall products through predictive capability in hydrological runoff modelling

The emergence of regional and global satellite‐based rainfall products with high spatial and temporal resolution has opened up new large‐scale hydrological applications in data‐sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite‐based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub‐catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC‐FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC‐FEWS, produced good results with values of R²_NS between 0·84 and 0·87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products. Copyright © 2009 John Wiley & Sons, Ltd.     

Ice and Snow Cover of Continental Water Bodies from Simultaneous Radar Altimetry and Radiometry Observations

We show how the studies of ice and snow cover of continental water bodies can benefit from the synergy of more than 15 years-long simultaneous active (radar altimeter) and passive (radiometer) observations from radar altimetric satellites (TOPEX/Poseidon, Jason-1, ENVISAT and Geosat Follow-On) and how this approach can be complemented by SSM/I passive microwave data to improve spatial and temporal coverage. Five largest Eurasian continental water bodies—Caspian and Aral seas, Baikal, Ladoga and Onega lakes are selected as examples. First we provide an overview of ice regime and history of ice studies for these seas and lakes. Then a summary of the existing state of the art of ice discrimination methodology from altimetric observations and SSM/I is given. The drawbacks and benefits of each type of sensor and particularities of radiometric properties for each of the chosen water bodies are discussed. Influence of sensor footprint size, ice roughness and snow cover on satellite measurements is also addressed. A step-by-step ice discrimination approach based on a combined use of the data from the four altimetric missions and SSM/I is presented, as well as validation of this approach using in situ and independent satellite data in the visible range. The potential for measurement of snow depth on ice from passive microwave observations using both altimeters and SSM/I is addressed and a qualitative comparison of in situ snow depth observations and satellite-derived estimates is made.     

Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates

Rainfall measurements by conventional raingauges provide relatively accurate estimates at a few points of a region. The actual rainfield can be approximated by interpolating the available raingauge data to the remaining of the area of interest. In places with relatively low gauge density such interpolated rainfields will be very rough estimates of the actual events. This is especially true for tropical regions where most rainfall has a convective origin with high spatial variability at the daily level. Estimates of rainfall by remote sensing can be very useful in regions such as the Amazon basin, where raingauge density is very low and rainfall highly variable. This paper evaluates the rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) satellite over the Tapajós river basin, a major tributary of the Amazon. Three-hour TRMM rainfall estimates were aggregated to daily values and were compared with catch of ground-level precipitation gauges on a daily basis after interpolating both data to a regular grid. Both daily TRMM and raingauge-interpolated rainfields were then used as input to a large-scale hydrological model for the whole basin; the calculated hydrographs were then compared to observations at several streamgauges along the river Tapajos and its main tributaries. Results of the rainfield comparisons showed that satellite estimates can be a practical tool for identifying damaged or aberrant raingauges at a basin-wide scale. Results of the hydrological modeling showed that TRMM-based calculated hydrographs are comparable with those obtained using raingauge data.     

Monitoring air pollution in China from geostationary satellite: A synthetic study using simulated observations

We simulated geostationary satellite observations to assess the potential for high spatial-and temporal-resolution monitoring of air pollution in China with a focus on tropospheric ozone(O_3), nitrogen dioxide(NO_2), sulfur dioxide(SO_2), and formaldehyde(HCHO). Based on the capabilities and parameters of the payloads onboard sun-synchronous satellites, we simulated the observed spectrum based on a radiative transfer model using a geostationary satellite model. According to optimal estimation theory, we analyzed the sensitivities and retrieval uncertainties of the main parameters of the instrument for the target trace gases. Considering the retrieval error requirements of each trace gas, we determined the major instrument parameter values(e.g., observation channel, spectral resolution, and signal-to-noise ratio). To evaluate these values, retrieval simulation was performed based on the three-dimensional distribution of the atmospheric components over China using an atmospheric chemical transportation model. As many as 90% of the experiments met the retrieval requirements for all target gases. The retrieval precision of total-column and stratospheric O_3 was 2%. In addition, effective retrieval of all trace gases could be achieved at solar zenith angles larger than 70°. Therefore, the geostationary satellite observation and instrument parameters provided herein can be used in air pollution monitoring in China. This study offers a theoretical basis and simulation tool for improving the design of instruments onboard geostationary satellites.