Analysis of TRMM Precipitation Radar Algorithms and Rain over the Tropics and Southeast Texas

The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A23 algorithm classifies rain echo as stratiform or convective while the 2A25 algorithm corrects vertical profiles of radar reflectivity for attenuation and calculates rain rates associated with the attenuation-corrected reflectivity. Updates to the 2A23 algorithm for Version 7 (V7) have resulted in an increase (decrease) in the fraction of rain echo classified as convective (stratiform) compared with previous versions of the algorithm. The tropics-wide (20°N-20°S) stratiform rain fraction has decreased correspondingly, which has implications for studying the impact of convection on the large-scale circulation because of the elevated heating associated with stratiform rain. Updates to the 2A25 algorithm have resulted in changes in the rain rates derived from radar reflectivity, with convective rain over land increasing between V6 and V7. Drop size distributions (DSD) from 2A25 are compared to rainfall data collected at two ground instrument sites in southeast Texas and show that the TRMM PR is still likely underestimating heavy rain rates over land, with implications for quantifying flash flood events and model evaluations of rain rate distributions

Comparison and Validation of Tropical Rainfall Measuring Mission (TRMM) Rainfall Algorithms in Tropical Cyclones

Tropical Rainfall Measuring Mission (TRMM) rainfall retrieval algorithms are evaluated in tropical cyclones (TCs). Differences between the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) retrievals are found to be related to the storm region (inner core vs. rainbands) and the convective nature of the precipitation as measured by radar reflectivity and ice scattering signature. In landfalling TCs, the algorithms perform differently depending on whether the rainfall is located over ocean, land, or coastal surfaces. Various statistical techniques are applied to quantify these differences and identify the discrepancies in rainfall detection and intensity. Ground validation is accomplished by comparing the landfalling storms over the Southeast US to the NEXRAD Multisensor Precipitation Estimates (MPE) Stage-IV product. Numerous recommendations are given to algorithm users and developers for applying and interpreting these algorithms in areas of heavy and widespread tropical rainfall such as tropical cyclones

The hydrology of the Peruvian Amazon river and its sensitivity to climate change

This PhD thesis explores the utility of a land surface model (Joint UK Land-Environment Simulator, JULES) for large-scale hydrological modelling of the Peruvian Amazon - a humid tropical mountain basin where process understanding is poor and data are scarce. A sparse rain gauge network necessitates the use of large-scale data from satellite and global climate model reanalysis to complement ground observations, commanding a closer look at (1) the uncertainties (2) merging techniques to utilise multiple observations in the model forcing. A main outcome of the research is establishing the model’s sensitivity to precipitation error, and at the same time, demonstrating an increasing reliability of global remote sensing products as model forcing, specifically, with data from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis version 7 algorithm. Furthermore, satellite-rain gauge data assimilation techniques such as mean-bias correction, double smoothing residual blending, and Bayesian combination, are shown to reduce the mean errors in the satellite-based product. Secondly, with regional calibration and an offline runoff routing scheme, JULES is shown to be reasonably skillful at reproducing the observed streamflow dynamic and extremes. Representing the subgrid heterogeneity of soil moisture using the probability distributed model (PDM) was key to improving surface runoff generation. However, evapotranspirative fluxes in the lower basin remain poorly reproduced without an adequate floodplain system representation. Finally, under the Intergovernmental Panel for Climate Change’s RCP4.5 future climate scenario, which projects a warming and wetting up to the year 2035, the Peruvian Amazon basin is shown to respond nonlinearly to the increase in wet season precipitation with more than 40% increase in the peak flows compared to the baseline scenario. There is limited confidence in the projections due to climate projections uncertainty and the assumptions of model stationarity.Open Acces

Climatology and Interannual Variability of Quasi-Global Intense Precipitation Using Satellite Observations

Climatology and variations of recent mean and intense precipitation over a near-global (50 deg. S 50 deg. N) domain on a monthly and annual time scale are analyzed. Data used to derive daily precipitation to examine the effects of spatial and temporal coverage of intense precipitation are from the current Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7 precipitation product, with high spatial and temporal resolution during 1998 - 2013. Intense precipitation is defined by several different parameters, such as a 95th percentile threshold of daily precipitation, a mean precipitation that exceeds that percentile, or a fixed threshold of daily precipitation value [e.g., 25 and 50 mm day(exp -1)]. All parameters are used to identify the main characteristics of spatial and temporal variation of intense precipitation. High correlations between examined parameters are observed, especially between climatological monthly mean precipitation and intense precipitation, over both tropical land and ocean. Among the various parameters examined, the one best characterizing intense rainfall is a fraction of daily precipitation Great than or equal to 25 mm day(exp. -1), defined as a ratio between the intense precipitation above the used threshold and mean precipitation. Regions that experience an increase in mean precipitation likely experience a similar increase in intense precipitation, especially during the El Nino Southern Oscillation (ENSO) events. Improved knowledge of this intense precipitation regime and its strong connection to mean precipitation given by the fraction parameter can be used for monitoring of intense rainfall and its intensity on a global to regional scale

Analysis and Prediction of the West African Monsoon Onset

The West African Monsoon onset marks a vital point in the seasonal monsoon cycle over the region with direct implications for local farmers and other stakeholders. In this work, valuable insight into the exact definition of the monsoon onset, its level of spatial consistency and cause of inter-annual variability of onsets has been presented. Criteria are presented to determine the value of monsoon onset definitions. There exist over seventeen unique onset definitions in publication. In this work, a representative sub-set of definitions have been compared to assess the relative value and suitability of onset definitions. It is found that the length scale over which a definition is defined determines the relevance to certain users. Local farmers require knowledge on local onset definitions which often have no similarity to regional onset definitions. Local onset dates are shown to have a pragmatic level of spatial homogeneity. Local Onset Regions (LORs) are presented over which local onset variability can be studied using a representative time series of onset dates. Using LORs, it is found that the seasonal progression of the Inter-Tropical Front and the phase of the Madden-Julian Oscillation drive the inter-annual variability of local onsets. The late passage of the Inter-Tropical Front past a LOR is linked to later onset in that region. Furthermore, when the Madden-Julian Oscillation inhibits convection across the Guinea Coast, local onset dates tend to be earlier than climatology. Further research into predicting drivers of local onset variability are suggested. Finally, seasonal forecast models tend to under-predict the variability of onset dates across West Africa. There is little significant correlation between observed onset dates (regional or local) and forecasts. It is concluded that seasonal onset forecasts are currently of little value to forecast users in West Africa. Suggestions as to the cause of this limitation are discussed

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4&thinsp;km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.</p

Bias adjustment of satellite rainfall data through stochastic modeling: Methods development and application to Nepal

Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Sparse ground-based gauge networks do not provide a robust basis for interpolation, and the reliability of remote sensing products, although improving, is still imperfect. Current techniques to estimate precipitation rely on combining these different kinds of measurements to correct the bias in the satellite observations. We propose a novel procedure that, unlike existing techniques, (i) allows correcting the possibly confounding effects of different sources of errors in satellite estimates, (ii) explicitly accounts for the spatial heterogeneity of the biases and (iii) allows the use of non overlapping historical observations. The proposed method spatially aggregates and interpolates gauge data at the satellite grid resolution by focusing on parameters that describe the frequency and intensity of the rainfall observed at the gauges. The resulting gridded parameters can then be used to adjust the probability density function of satellite rainfall observations at each grid cell, accounting for spatial heterogeneity. Unlike alternate methods, we explicitly adjust biases on rainfall frequency in addition to its intensity. Adjusted rainfall distributions can then readily be applied as input in stochastic rainfall generators or frequency domain hydrological models. Finally, we also provide a procedure to use them to correct remotely sensed rainfall time series.We apply the method to adjust the distributions of daily rainfall observed by the TRMM satellite in Nepal, which exemplifies the challenges associated with a sparse gauge network and large biases due to complex topography. In a cross-validation analysis on daily rainfall from TRMM 3B42 v6, we find that using a small subset of the available gauges, the proposed method outperforms local rainfall estimations using the complete network of available gauges to directly interpolate local rainfall or correct TRMM by adjusting monthly means. We conclude that the proposed frequency-domain bias correction approach is robust and reliable compared to other bias correction approaches. © 2013 Elsevier Ltd

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products