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E. J. Barton, C. M. Taylor, C. Klein, P. P. Harris, and X. Meng

observations (lower uncertainties, less missing data) compared to SM, the bulk of our analysis uses LST. Previous works (e.g., T11 ; T15 ) have used LST anomalies (LSTAs) as a proxy for surface wetness conditions. Locally positive (negative) LSTAs are indicative of locally drier (wetter) conditions compared to the surroundings. This study employs LSTA observations from the 10-km AMSR2 downscaled LPRM Level 2 product (overpass time 1530 LT, UTC + 8; de Jeu and Owe 2014 ; Owe et al. 2008 ). The microwave

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Yali Luo, Weimiao Qian, Renhe Zhang, and Da-Lin Zhang

borders of the provinces stationed around the analysis domain, along with Yangtze, Huai, and Yellow rivers (green lines) and the location of Wangjiaba reservoir (red cross). Shadings represent terrain heights. However, there has been the lack of such high-resolution precipitation observations to reveal detailed spatial and temporal distributions of the mei-yu precipitation in east China, and similarly for heavy precipitating MCSs occurring elsewhere around the world. Clearly, such high-resolution data

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Dimitrios Stampoulis, Emmanouil N. Anagnostou, and Efthymios I. Nikolopoulos

advanced national weather radar networks, the observational gaps over mountainous areas can be quite significant ( Maddox et al. 2002 ). As stated above, although radar-derived rainfall estimates are in many cases quite reliable, their coverage is still very limited relative to satellite observations (e.g., mountainous areas, developing regions like Africa, and parts of Asia), and this renders them less desirable for global hydrological applications. Also, deploying and maintaining a fully functional

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F. Chen, W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari

radiance observations (infrared and microwave) and ground-based radar precipitation observations (from 2009) to produce its reanalyzed precipitation field. Because of the potential cross-use of SSMIS and AMSU/MHS microwave observations in both the ERA5 and H23 products, it is necessary to consider the possibility of H23 and ERA5 estimates containing cross-correlated error. Such error cross dependence would violate a key TC assumption—see section 4a for further discussion. 3. Method a. Triple

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Rebecca Gugerli, Marco Gabella, Matthias Huss, and Nadine Salzmann

–538 . Carturan , L. , G. D. Fontana , and M. Borga , 2012 : Estimation of winter precipitation in a high-altitude catchment of the Eastern Italian Alps: Validation by means of glacier mass balance observations . Geogr. Fis. Din. Quat. , 35 , 37 – 48 , https://doi.org/10.4461/GFDQ.2012.35.4 . Clifford , D. , 2010 : Global estimates of snow water equivalent from passive microwave instruments: History, challenges and future developments . Int. J. Remote Sens. , 31 , 3707 – 3726 , https

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Abedeh Abdolghafoorian and Paul A. Dirmeyer

. Only one global observational dataset for soil moisture has been used. Our investigations show that including the CCI radiometer-only (PASSIVE) product (which combines only passive microwave soil moisture products) and the CCI scatterometer-only (ACTIVE) product (which combines only single-sensor active soil moisture products), in addition to the COMBINED CCI product, does not change the conclusion on a comparison between consistency of observations versus models. Spatial correlations of both

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Chris Kidd, Erin Dawkins, and George Huffman

those from infrared (IR) sensors. These observations are available at relatively high resolutions (1–4 km) with temporal samples as frequent as every 15 min. However, since IR techniques are based on cloud top characteristics, they are also the most indirect measure of precipitation at the surface. Well-calibrated passive microwave (PMW) observations have been available since mid-1987 and provide a more direct measure of precipitation, although at the expense of temporal sampling. The most direct

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Jorge L. Peña-Arancibia, Albert I. J. M. van Dijk, Luigi J. Renzullo, and Mark Mulligan

European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim; Dee et al. 2011 ), and 3) the Japanese 25-yr Reanalysis (JRA-25; Onogi et al. 2007 ). These reanalyses build and improve on earlier reanalysis versions by improving the forecasting model physics and incorporating new satellite and other conventional data. Also included are three quasi-global satellite-based precipitation products that combine multiple microwave and infrared sensors: 1) the bias

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Rebecca A. Smith and Christian D. Kummerow

regions, satellite-derived precipitation datasets are limited. Microwave precipitation estimates are not as accurate over land (particularly over mountainous regions), and land infrared (IR) precipitation estimates are not provided over regions with snow cover owing to the brightness temperature of snow closely matching the brightness temperature of cloud tops. Over the UCRB, snow cover is an issue for over six months of the year, limiting the amount of satellite precipitation observations. The

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Pravat Jena, Sourabh Garg, and Sarita Azad

based on microwave observations, and further, these estimates are interpolated by the motion vectors derived from infrared observations. Using this, precipitation accumulation estimates at different temporal scales (multihours) have been improved compared to the simple averaging of microwave-based estimates, which incorporate microwave and infrared information. The precipitation estimates are derived from the four types of passive microwave instruments, namely, AMSU-B, AMSR-E ( Ferraro et al. 2000

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