We study the space-time characteristics of global tropical rainfall. The data used is from the Tropical Rainfall Measuring Mission (TRMM) and spans the years 2000-2009. Using anomaly fields constructed by removing a single mean and by subtracting the climatology of the ten year dataset, we extract the dominant modes of variability of tropical rainfall from an Empirical Orthogonal Function (EOF) analysis. To our knowledge, this is the first attempt at applying the EOF formal-ism to high spatio-temporal resolution global tropical rainfall. Spatial patterns and temporal indices obtained from the EOF analysis with single annual mean removed show large scale patterns associated with the seasonal cycle. Even though the seasonal cycle is dominant, the principal component (PC) time series show fluctuations at subseasonal scales. When the climatological mean is removed, spatial patterns of the dominant modes resemble features associated with tropical intraseasonal variability (ISV). Correspondingly, the signature of a seasonal cycle is relatively suppressed, and the PCs have prominent fluctuations at subseasonal scales. The significance of the leading EOFs is demonstrated by means of a novel ratio plot of the variance captured by the leading EOFs to the variance in the data. This shows that, in regions of high variability (which go hand in hand with high rainfall), the EOF/PC pairs capture a fair amount of the variance (up to 20% for the first EOF/PC pair) in the data. We then pursue an EOF analysis of the finest data resolution available. In particular, we per-form a regional analysis (a global analysis is beyond our present computational resources) of the tropics with 0.25◦×0.25◦, 3-hourly data. The regions we focus on are the Indian region, the Maritime Continent and South America. The spatial patterns obtained reveal a rich hierarchical structure to the leading modes of variability in these regions. Similarly, the PCs associated with these leading spatial modes show variability all the way from 90 days to the diurnal scale. With the results from EOF analysis in hand, we quantify the multiscale spatio-temporal structures encountered in our study. In particular, we examine the power spectra of the PCs and EOFs. A robust feature of the space and time spectra is the distribution of energy or variance across a range of scales. On the temporal front, aside from a seasonal and diurnal peaks, the variance scales as a power-law from a few days to the 90 day period. Similarly, below the planetary scale, from approximately 5000 km to 200 km the spatial spectrum also follows a power-law. Therefore, when trying to understand the variability of tropical rainfall, all scales are important, and it is difficult to justify a focus on isolated space and time scales.

No description available.

The fine-scale structure of global (30N-30S) tropical rainfall is characterised using 13 years (1998-2010) of 3-hourly and daily, 0.25-degree Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall product. At the outset, the dominant timescales present in rainfall are identified. Specifically, the Fourier spectrum (in time) is estimated in two ways (a) spectrum of spatially averaged (SoSA) rainfall; and (b) spatial average of the spectrum (SAoS) of rainfall at each grid point. This procedure is applied on rainfall at the 3-hourly and daily temporal resolutions. Both estimates of the spectrum show the presence of a very strong seasonal cycle. But, at subseasonal timescales, the two methods of estimating spectrum show a marked difference in daily rainfall. Specifically, with SoSA the variability peaks at a subseasonal timescale of around 5 days, with a possible secondary peak around 30-40 days (mostly in the southern tropics). With SAoS, the variability is distributed across a range of timescales, from 2 days to 90 days. However, with finer resolution (3-hourly) observations, it is seen that (besides the seasonal cycle) both methods agree and yield a dominant diurnal scale. Along with other subseasonal scales, the contribution and geographical distribution of diurnal scale variability is estimated and shown to be highly significant. Given its large contribution to the variability of tropical rainfall, the diurnal cycle is extracted by means of a Fourier-based filtering and analysed. The diurnal rainfall anomaly is constructed by eliminating all timescales larger than 1 day. Following this, taking care to avoid spurious peaks associated with Gibbs oscillations, the time of day (called the peak octet) when the diurnal anomaly is largest is identified. The peak octet is estimated for each location in the global tropics. This is repeated for 13 years, and the resulting mode of the time of maximum rainfall is established. It is seen that (i) most land regions receive rainfall during the late afternoon/early evening hours; (ii) rainfall over open oceans lack a dominant diurnal signature with a possible combination of early morning and afternoon showers; (iii) coastal regions show a clear south/southwest propagation in the mode of the peak octet of rainfall. In addition to being a comprehensive documentation of the diurnal cycle at very fine scales, the results serve as a critical test for the validation of theoretical and numerical models of global tropical rainfall.

Made available in DSpace on 2015-05-14T12:09:33Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 7249641 bytes, checksum: 530262ccdecfb3b77d7356a51a29f342 (MD5) Previous issue date: 2014-05-15 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The spatial and temporal variability is a precipitation feature and constitutes a factor of complexity for developing rainfall studies. Moreover, the low density of rain gauge stations and errors in data collection in the field increase the difficulties in implementing studies in this research area. However, such researches are essential considering that it is from them that we can carry out flood and drought forecasts, understand the hydrological regime of rivers, soil moisture, temperature changes, among others. Thus, the spatial rainfall estimates obtained through satellites data are important because, although present uncertainties, when compared with punctual data measured in the field can provide good indicators of the spatial distribution of rainfall for a given area. In this research, we evaluate the potential of rainfall estimates from TRMM (Tropical Rainfall Measuring Mission) sensor to represent the spatio-temporal variability of precipitation in the State of Paraíba, in the Northeast of Brazil. In this study we considered daily time series of 14 years length of rainfall data collected by AESA (Agência Executiva de Gestão das Águas do Estado da Paraíba) in 269 rainfall gauges and rainfall data estimated from TRMM satellite for a spatial mesh of 198 grid points covering the Paraíba State and which have been interpolated to the rain gauge locations using the inverse squared distance method. Comparisons were made considering the accumulated rainfall in different periods of time: daily, three days, seven days and monthly. With respect to spatial factors, the comparisons were developed based on punctual values in rain gauges stations, areal averages over sub-basins and mesoregions, and topographic profile. The statistical analyzes of comparison between the observed and estimated rainfall were developed based on the average rainfall, the linear correlations, the mean absolute error and root mean square error considering each accumulated period. Regarding the daily precipitation, the majority of the rain gauges (91%) showed correlation coefficients ranging from 0.5 to 0.7. This correlation increases for considering 3 days-rainfall, with values ranging from 0.5 to 0.7 in 56% of rain gauges, and of 0.7-0.8 for 42% of rain gauges. For the 7 days-rainfall, 58% of the rain gauges presented correlations ranging from 0.7 to 0.8, while for the monthly rainfall 95% of the rain gauges obtained correlations higher than 0.8. Therefore, the results indicate that the TRMM satellite provides better estimates when data are accumulated in larger time intervals. The monthly analysis showed that March and April are the months with higher correlation between observed and estimated precipitation, and that in the first months of the year the estimated and observed values have better approximations for all types of analyzes. It was also verified a good estimation potential in the analysis of seasonal variability of precipitation. Moreover, it was observed that the satellite presents the largest errors in the areas with the largest amount of rainfall. In the sub-basins and in the mesoregions of the state the rainfall regime was estimated quite closely. We concluded that the TRMM satellite presents very good skill in reproducing the observed rainfall measured in the gauge stations over the Paraíba state, becoming an important data source for helping the water resources planning and decision making / A variabilidade temporal e espacial, que é um elemento característico da precipitação pluvial se configura como um fator de complexidade para as pesquisas sobre chuvas. Além disso, a baixa densidade de postos pluviométricos e os equívocos nos processos de coleta em campo aumentam as dificuldades na execução de estudos nessa área de pesquisa. No entanto, tais pesquisas são essenciais tendo em vista que é a partir delas que se pode fazer previsão de enchentes e estiagens, compreender o regime hidrológico dos rios, a umidade do solo, as mudanças de temperatura, dentre outras. Assim, as estimativas espaciais de precipitação realizadas por satélites são técnicas importantes, pois, embora contenham incertezas, quando comparadas com valores pontuais medidos em solo podem fornecer bons indicativos da distribuição espacial das chuvas para uma determinada área. Nesta pesquisa, avalia-se o potencial das estimativas de chuva do satélite TRMM, versão 7 e 3B42 (Tropical Rainfall Measuring Mission) para representar a variabilidade espaço-temporal da precipitação no Estado da Paraíba, no Nordeste do Brasil. No estudo considerou-se séries temporais de dados diários para um período de 14 anos (1998-2011) fornecidas pela AESA (Agência Executiva de Gestão das Águas do Estado da Paraíba) referentes a 269 postos pluviométricos e dados estimados pelo satélite TRMM numa malha espacial de 198 pontos que cobrem o Estado da Paraíba e que foram interpolados para os locais de observação de campo pelo método do inverso do quadrado da distância. As comparações foram realizadas considerando a chuva acumulada em diferentes períodos: diário, três dias, sete dias e mensal. Com relação aos fatores espaciais, os comparativos foram desenvolvidos com base em valores pontuais nos locais de observação, médias espaciais considerando sub-bacias, mesorregiões, e perfil topográfico. As análises estatísticas de comparação entre a chuva observada e a estimada foram desenvolvidas a partir das médias de chuva, das correlações lineares, do erro médio absoluto e da raiz do erro médio quadrático considerando cada período acumulado. Nas análises da chuva diária a maioria dos postos (91%) apresentou índices de correlação variando de 0,5 a 0,7. Esta correlação aumenta para os acumulados de 3 dias, com valores que variam de 0,5 a 0,7 em 56% dos postos pluviométricos e de 0,7 a 0,8 em 42% dos postos. Nos acumulados de 7 dias, 58% dos pluviômetros apresentaram correlações que variam de 0,7 a 0,8 e nos acumulados mensais 95% dos postos apresentam correlações superiores a 0,8. Portanto, os resultados indicam que o satélite TRMM apresenta melhores estimativas quando os dados estão acumulados em intervalos maiores de tempo. Na análise mensal verificou-se que março e abril são os meses mais significativos de estimação e que nos primeiros meses do ano os valores estimados e observados apresentam melhores aproximações para todos os tipos de análises. Identificou-se também bom potencial de estimação na análise da variabilidade sazonal de precipitação. Além disso, observou-se que o satélite apresenta os maiores erros para as áreas onde ocorrem os maiores volumes de chuva. Nas sub-bacias e nas mesorregiões do Estado, o regime de chuvas foi estimado com bastante fidelidade em todas as formas analisadas. Conclui-se que o satélite TRMM apresenta bom desempenho para reproduzir as chuvas observadas em pluviômetros no Estado da Paraíba, configurando-se como uma importante fonte de dados para o auxílio no planejamento e na tomada de decisões relativas aos recursos hídricos

No description available.

The work presented in the thesis focuses on systematically documenting the multi scale nature of the temporal persistence and spatial coherence of tropical rainfall. There are three parts to the thesis: The first two parts utilize satellite-retrieved rainfall at multiple observational resolutions to characterize the space-time organization of rain; the third part assesses the ability of state-of-the-art coupled models to reproduce some of the observed features. In the first part of the study, which focuses on the temporal persistence of rain, we analyze the Tropical Rainfall Measurement Mission (TRMM) satellite-based observations to compare and contrast wet and dry spell characteristics over the tropics (30 S-30 N). Defining a wet (dry) spell as the number of consecutive rainy (nonrainy) days, we find that the distributions of wet spells (independent of spatial resolution) exhibit universality in the following sense. While both ocean and land regions with high seasonal rainfall accumulation (humid regions) show a predominance of 2-4 day wet spells, those regions with low seasonal rainfall accumulation (arid regions) exhibit a wet spell duration distribution that is essentially exponential in nature, with a peak at 1 day. The behaviour that we observed for wet spells is reversed for dry spell distributions. The total rainfall accumulated in each wet spell has also been analyzed, and we find that the major contribution to seasonal rainfall for arid regions comes from very short length wet spells; however, for humid regions, this contribution comes from wet spells of duration as long as 30 days. An exhaustive sensitivity study of factors that can potentially affect the wet and dry spell characteristics (e.g., resolution) shows that our findings are robust. We also explore the role of chance in determining the 2-4 day mode, as well as the inuence of organized convection in separating reality from chance. The second part deals with the spatial coherence of tropical rain. We take two different approaches, namely, a global and local view. The global view attempts to quantify the con-ventional view of rain, i.e., the dominance of the intertropical convergence zone (ITCZ), while the local view tries to answer the question: if it rains, how far is the influence felt in zonal and meridional directions? In both approaches, the classical e-folding length for spatial decorrelation is used as a measure of spatial coherence. The major finding in the global view approach is that, at short timescales of accumulation (daily to pentad to even monthly), rain over the Equator shows the most dominant zonal scale. It is only at larger timescales of accumulation (seasonal or annual) that the dominance of ITCZ around 7 N is evident. In addition, we also find a semi-log linearity between the spatial scales, seen from afar, and timescale of accumulation, with a break in linearity around typical synoptic timescales of 5-10 days. The local view quantifies the dominance of the zonal scale in the tropical ocean convergence zones, with an anisotropy value (ratio of zonal to meridional scales) of 3-4. Over land, on the other hand, the zonal and meridional scales are comparable in magnitude, suggesting that rain tends to be mostly isotropic over continental regions. This latter finding holds true, irrespective of the spatial and temporal resolutions at which rain is observed. Interestingly, the anisotropy over ocean, while invariant with spatial resolution, is found to be a function of temporal resolution: from a value of 3-4 at daily timescale, it decreases to around 1.5 at 3-hourly resolution, suggesting that perhaps rain fundamentally might be isotropic in nature at an event scale. The final part analyses a few models from the suite of Coupled Model Intercomparison Project (CMIP5) models, to evaluate their ability to reproduce some of these aforementioned features. For all the strong biases that models are known to have, some of the observed features are captured well by the models. Specifically, on the temporal persistence front, the observed 2-4 day mode of wet (dry) spells of rain over humid (arid) regions is also seen in models. The overestimation of longer duration wet spells appears to be the primary cause of a positive bias in the number of rainy days from the models. In general, the tendency of models to not stop raining results in lower and higher number of shorter and longer duration wet spells, respectively, and consequently an overall reduction in dry spells of all durations. On the spatial coherence front, the main finding from the global view approach is that the observed semi-log linearity of the zonal spatial scale of rainfall as a function of timescale of accumulation is strikingly well-reproduced by the models. Even more remarkable is that the models are able to mimic the break in this linearity around 5 days (typical synoptic scale). What the models fail to do prominently is the transition of the dominance of equatorial rain at smaller timescales of accumulation to the dominance of ITCZ at around 7 N at higher timescales of accumulation. Based on the local view approach, we find that, in general, even though the zonal and meridional scales are overestimated, the observed isotropy of continental rain is captured very well by the models. Over the oceans, the success is less prominent, especially with the core of the ITCZ showing much larger ratios than those observed. Thus, the models seem to be able to reproduce the anisotropy for the wrong reasons, and the proposed anisotropy ratio could be a useful metric in further diagnosis of climate models.

In first part of the study we discuss impact of dynamical core in simulation of tropical rainfall. Over years many new dynamical cores have been developed for atmospheric models to increase efficiency and reduce numerical errors. CAM3.1 gives an opportunity to study the impact of the dynamical core on simulations with its three dynamical cores namely Eulerian spectral(EUL) , Semilagrangian dynamics(SLD) and Finite volume(FV) coupled to a single parametrization package. A past study has compared dynamical cores of CAM3 in terms on tracer transport and has showed advantages using FV in terms of tracer transport. In this study we compare the dynamical cores in climate simulations and at their optimal configuration, which is the intended use of the model. The model is forced with AMIP type SST and rainfall over seasonal, interannual scales is compared. The significant differences in simulation of seasonal mean exist over tropics and over monsoon regions with observations and among dynamical cores. The differences among EUL and SLD, which use spectral transform methods are lesser compared that of with FV clearly indicating role of numerics in differences. There exist major errors in simulation of seasonal cycle in all dynamical cores and errors in simulation of seasonal means over many regions are associated with errors in simulation of seasonal cycle such as over south china sea. Seasonal cycle in FV is weaker compared to SLD and EUL. The dynamical cores exhibit different interannual variability of rainfall over Indian monsoon region, the period of maximum power corresponding to a dynamical core differs substantially with another. From this study there seems no superiority associated with FV dynamical core over all climate scales as seen in tracer transport. The next part of the study deals with impact of diurnal ocean atmosphere coupling in an AGCM,CAM3.1. Due to relatively low magnitude of diurnal cycle of SST and lack of SST observations over diurnal scales current atmospheric models are forced with SSTs of periods grater than a day. CAM 3.1 standalone model is forced with monthly SSTs but the interpolation is linear to every time step between any two months and this linear interpolation implies a linear diurnal and intraseasonal variation of SST which is not true in nature. To test the sensitivity of CAM3.1 to coupling of SST on diurnal scales, we prescribed over tropics(20S20N) a diurnal cycle of SST over daily mean interpolated SST of different magnitudes and phase comparable to observations. This idea of using a diurnal cycle of SST retaining seasonal mean SST in an atmospheric model is novel and provides an interesting frame work to test sensitivity of model to interpolations used in coupling of boundary conditions. Our analysis shows a high impact of using diurnal cycle of SST on simulation of mean rainfall over tropics. The impact in a case where diurnal cycle of SST is fixed and retained to daily mean SST implies that changes associated with a coupled model are to some extent due to change in representation of diurnal cycle of SST. A decrease of excess rainfall over western coast of Bay of Bengal and an increase of rainfall over northern bay of Bengal in such case is similar to the improvement due to coupling atmospheric model to a slab ocean model. This also implies that problems with current AMIP models in simulation of seasonal mean Indian monsoon rainfall could be due to erroneous representation of diurnal cycle of SST in models over this region where the diurnal cycle of SST is high in observations. The high spatial variability of the impact in various cases over tropics implies that a similar spatial variation of diurnal cycle could be important for accurate simulation of rainfall over tropics. Preliminary analysis shows that impact on rainfall was due to changes in moisture convergence. We also hypothesized that diurnal cycle of SST could trigger convection over regions such as northern Bay of Bengal and rainfall convergence feedback sustains it. The impact was also found on simulation of internal interannual variability of rainfall

In this thesis, we investigated modes of intraseasonal variability (ISV) observed in the Indian monsoon rainfall and how these modes modulate rainfall over India. We identified a decreasing trend in the intensity of low-frequency intraseasonal mode with increasing strength in synoptic variability over India. We also made an attempt to understand the reason for these observed trends using numerical simulations. In the first part of the thesis, satellite rainfall estimates are used to understand the spatiotem-poral structures of convection in the intraseasonal timescale and their intensity during boreal sum-mer over south Asia. Two dominant modes of variability with periodicities of 10–20-days (high-frequency) and 20–60-days (low-frequency) are found, with the latter strongly modulated by sea surface temperature. The 20–60-day mode shows northward propagation from the equatorial In-dian Ocean linked with eastward propagating modes of convective systems over the tropics. The 10–20-day mode shows a complex space-time structure with a northwestward propagating anoma-lous pattern emanating from the Indonesian coast. This pattern is found to be interacting with a structure emerging from higher latitudes propagating southeastwards. This could be related to ver-tical shear of zonal wind over northern India. The two modes exhibit variability in their intensity on the interannual time scale and contribute a significant amount to the daily rainfall variability in a season. The intensities of the 20–60-day and 10–20-day modes show significantly strong inverse and direct relationship, respectively, with the all-India June–September rainfall. This study also establishes that the probability of occurrence of substantial rainfall over central India increases significantly if the two intraseasonal modes simultaneously exhibit positive anomalies over the region. There also exists a phase-locking between the two modes. In the second part of the thesis, we investigated the changing nature of these intraseasonal modes over Indian region, and their association with extreme rainfall events using ground based observed rainfall. We found that the relative strength of the northward propagating 20–60-day mode has a significant decreasing trend during the past six decades, possibly attributed to the weakening of large-scale circulation in the region during monsoon. This reduction is compensated by a gain in synoptic-scale (3–9 days) variability. The decrease in the low-frequency ISV is associated with a significant decreasing trend in the percentage of extreme events during the active phase of the monsoon. However, this decrease is balanced by a significant increasing trend in the percentage of extreme events in break phase. We also find a significant rise in occurrence of extremes during early- and late-monsoon months, mainly over the eastern coastal regions of India. We do not observe any significant trend in the high-frequency ISV. In the last part of the thesis, we used numerical simulations to understand the observed changes in the ISV features. Using the atmospheric component of a global climate model (GCM), we have performed two experiments: control experiment (CE) and heating experiment (HE). The CE is the default simulation for 10 years. In HE, we prescribed heating in the atmosphere in such a way that it mimics the conditions for extreme rainfall events as observed over central India during June– September. Heating is prescribed primarily during the break phase of the 20–60-day mode. This basically increases the number of extremes, majority of which are in break phase. The design of the experiment reflects the observed current scenario of increased extreme events during breaks. We found that the increased extreme events in the HE decreased the intensity of the 20–60-day mode over the Indian region. This reduction is associated with a reduction of rainfall in active phase and increase in the length of break phase. A reduction in the seasonal mean over India is also observed. The reduction of active phase rainfall is linked with an increased stability of the atmosphere over central India. Lastly, we propose a possible mechanism for the reduction of rainfall in active phase. We found that there is a significant reduction in the strength of the vertical easterly shear over the northern Indian region during break–active transition phase. This basically weakens the conditions for the growth of Rossby wave instability, thereby elongating break phase and reducing the rainfall intensity in the following active phase. This study highlights the redistribution of rainfall intensity among periodic (low-frequency) and non-periodic (extreme) modes in a changing climate scenario, which is further tested in a modeling study. The results presented in this thesis will provide a pathway to understand, using observations and numerical model simulations, the ISV and its relative contribution to the Indian summer monsoon. It can also be used for model evaluation.

In this thesis, we have presented a systematic analysis of the change of cloud properties due to variation in aerosol concentration over Indian region using satellite observations, and Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) simulations. The Tropical Rainfall Measurement Mission (TRMM) based Microwave Imager (TMI) estimates (2A12) have been used to compare and contrast the characteristics of cloud liquid water and ice over the Indian land region and the surrounding oceans, during the pre-monsoon (May) and monsoon (June–September) seasons. Based on the spatial homogeneity of rainfall, we have selected five regions for our study (three over ocean, two over land). In general, we find that the mean cloud liquid water and cloud ice content of land and oceanic regions are different, with the ocean regions showing higher amount of CLW. A comparison across the ocean regions suggests that the cloud liquid water over the or graphically influenced Arabian Sea (close to the Indian west coast) behaves differently from the cloud liquid water over a trapped ocean (Bay of Bengal) or an open ocean (Equatorial Indian Ocean). Specifically, the Arabian Sea region shows higher liquid water for a lower range of rainfall, whereas the Bay of Bengal and the Equatorial Indian Ocean show higher liquid water for a higher range of rainfall. Apart from geographic differences, we also documented seasonal differences by comparing cloud liquid water profiles between monsoon and pre-monsoon periods, as well as between early and peak phases of the monsoon. We find that the cloud liquid water during the lean periods of rainfall (May or June) is higher than during the peak and late monsoon season (July-September) for raining clouds over central India. However, this is not true over the ocean. As active and break phases are important signatures of the monsoon progression, we also analyzed the differences in cloud liquid water during various phases of the monsoon, namely, active, break, active-to-break (a2b) and break-to-active (b2a) transition phases. We find that the cloud liquid water content during the b2a transition phase is significantly higher than that during the a2b transition phase over central India. We speculate that this could be attributed to higher amount of aerosol loading over this region during the break phase. We lend credence to this aerosol-liquid water/rain association by comparing the central Indian cloud liquid water with Southeast Asia (where the aerosol loading is significantly smaller) and find that in the latter region, there are no significant differences in cloud liquid water during the different phases of their monsoon. The second part of our study involves evaluating the ability of the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to simulate the observed variation of cloud liquid water and rain efficiency. We have used no chemistry option, and the model was run with constant aerosol concentration. The model simulations (at 4.5 km resolution) are done for the month of June–July 2004 since this period was particularly favorable for the study of an active–break cycle of the monsoon. We first evaluate the sensitivity of the model to different parameterizations (microphysical, boundary layer, land surface) on the simulation of rain over central India and Bay of Bengal. This is done to identify an “optimal” combination of parameterizations which reproduces the best correlation with observed rain over these regions. In this default configuration (control run), where the aerosol concentration is kept constant throughout the simulation period, the model is not able to reproduce the observed variations of cloud liquid water during the different phases of an active-break cycle. To this end, we proceeded to modify the model by developing an aerosol-rain relation, using Aerosol Robotic Network (AERONET) and TRMM 3B42 data that realistically captures the variation of aerosol with rain. It is worth highlighting here that our goal was to primarily isolate the indirect effect of aerosols in determining the observed changes in cloud liquid water (CLW) during the active-break phases of the Indian monsoon, without getting into the complexity of a full chemistry model such as that incorporated in WRF-Chem. Moreover, the proposed modification (modified run) is necessitated by the lack of realistic emission estimates over the Indian region as well as the presence of inherent biases in monsoon simulation in WRF. The main differences we find between the modified and control simulations is in the mean as well as spatial variability of CLW. We find that the proposed modification (i.e., rate of change of aerosol concentration as a function of rain rate) leads to a realistic variation in the CLW during the active-break cycle of Indian monsoon. Specifically, the peak value of CLW in the b2a (a2b) phase is larger (smaller) in the modified as compared to the control run. These results indicate a stronger change in CLW amount in the upper levels between the two transition phases in the modified scheme as compared to the control simulation. More significantly, we also observe a change in sign at the lower levels of the atmosphere, i.e., from a strong positive difference in the control run to a negative difference in the modified simulation, similar to that observed. Additionally, we investigated the impact of the proposed modification, via CLW changes, on cloud coverage, size of clouds and their spatial variability. We find that the transformation of optically thin clouds to thick clouds during the break phase was associated with larger cloud size in modified compared to the control simulation. Moreover, the higher rate of decay of the spatial variability of CLW with grid resolution, using the modified scheme, suggests that clusters of larger clouds are more in the modified compared to control simulation. Taken together, the interactive aerosol loading proposed in this thesis yields model simulations that better mimic the observed CLW variability between the transition phases.

Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM TMI have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.