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The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product provided over 17 years of gridded precipitation datasets. However, the accuracy and spatial resolution of TMPA limits the applicability in hydrometeorological applications. We present a dataset that enhances the accuracy and spatial resolution of the TMPA monthly product (3B43). We resample the TMPA data to a 1 km grid and apply a correction function derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) to reduce bias in the data. We confirm a linear relationship between bias and elevation above 1,500 meters where TMPA underestimates measured precipitation, providing a proof-of-concept of how simple linear scaling can be used to augment existing satellite datasets. The result of the correction is the High-Resolution Altitude-Corrected Precipitation product (HRAC-Precip) for the CONUS. Using 9,200 precipitation stations from the Global Historical Climatology Network (GHCN), we compare the accuracy of TMPA 3B43 versus the new HRAC-Precip product. The results show an improvement of the mean absolute error of 12.98% on average., Measurement(s)hydrological precipitation processTechnology Type(s)digital curation • computational modeling techniqueFactor Type(s)temporal intervalSample Characteristic - EnvironmentweatherSample Characteristic - Locationcontiguous United States of Americ Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11823549

Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly to seasonal (M2S) top layer SM forecasts by forcing 1–3 month ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the Southeast US (SEUS) and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-months (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1–3 month lead times respectively. As a case study, the evaluation of the issued forecasts based on the drought indexes monitored during the 2007 historical drought over the SEUS also indicate promising skill in monthly SM forecasting to support agricultural planning and water management for such natural hazards.

We present a data set that enhanced the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) monthly product 3B43 in its accuracy and spatial resolution. For this, we developed a correction function to improve the accuracy of TRMM 3B43, spatial resolution of ~25 km, by estimating and removing the bias in the satellite data using a ground-based precipitation data set. We observed a strong relationship between the bias and land surface elevation; TRMM 3B43 tends to underestimate the ground-based product at elevations above 1500 m above mean sea level (m.amsl) over the conterminous United States. A relationship was developed between satellite bias and elevation. We then resampled TRMM 3B43 to the Digital Elevation Model (DEM) data set at a spatial resolution of 30 arc second (~1 km on the ground). The produced high-resolution satellite-based data set was corrected using the developed correction function based on the bias-elevation relationship. Assuming that each rain gauge represents an area of ~1 km2, we verified our product against 9,200 rain gauges across the conterminous United States. The new product was compared with the gauges, which have 50, 60, 70, 80, 90, and 100% temporal coverage within the TRMM period of 1998 to 2015. Comparisons between the high-resolution corrected satellite-based data and gauges showed an excellent agreement. The new product captured more detail in the changes in precipitation over the mountainous region than the original TRMM 3B43.

Drought has wreaked havoc to human societies throughout history and drought phenomenon is very complex as it is caused by a multitude of factors starting from the deficit of precipitation when compared to climatic mean. Such a deficit, when combined with sustained dry weather conditions entertain high evaporation and transpiration and thus result in soil moisture drought if there is no adequate irrigation. Finally, hydrological drought emerges by a combination of lack of precipitation and excessive use of available water resources for a sustained period. To assess historical droughts and to provide drought monitoring, various indices have been proposed for different purposes and using different datasets. Despite the panoply of these indices, when one focuses on the processes that caused droughts, a set of consistent indices may adequately describe the different aspects of the droughts.

Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil and replenishes the water table versus the amount that contributes to surface runoff and to channel flow. However observations of soil moisture at a point scale are very sparse and observing networks are expensive to maintain. Satellite sensors can observe large areas but the spatial resolution of these is dependent on microwave frequency, antenna dimensions, and height above the earth’s surface. The higher the sensor, the lower the spatial resolution and at low elevations the spacecraft would use more fuel. Higher spatial resolution requires larger diameter antennas that in turn require more fuel to maintain in space. Given these competing issues most passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applications. Most local applications require higher-spatial-resolution soil moisture data. Downscaling of the data requires ancillary data and model products, all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrology. In this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture.

To determine if repeated performance of endotracheal tube insertion via the intubating laryngeal airway (ILA) would shorten insertion time in mannequins.Prospective study.Clinical Skills Laboratory, Department of Anesthesia, Toronto Western Hospital.65 department anesthesiologists.After a video training session, anesthesiologists with no previous experience with the ILA performed 5 consecutive ILA-guided tracheal tube intubations on a mannequin. Each participant completed Task 1: insertion of an ILA; Task 2: blind insertion of a tracheal tube through the ILA, and Task 3: removal of the ILA. The time required for each task and the total intubation time for the three tasks over the 5 attempts were recorded. These times were compared using repeated-measures analysis of variance. The success rate among the 5 attempts was compared using Chi-Square analyses.A total of 65 anesthesiologists performed 5 ILA-guided tracheal intubations each. Total intubation time decreased from the first to the fifth attempt (92.6 ± 22.7 sec, 74.5 ± 19.2 sec, 66.5 ± 16.5 sec, 65.9 ± 19.9 sec, and 60.8 ± 16.3 sec; P0.001). Significant differences in intubation times were noted between the first and second, and the second and third attempts (P0.001 and P = 0.02, respectively). The success rate did not change over the 5 attempts (84.6%, 89.2%, 84.6%, 89.2%, and 90.8%; P = 0.737).Total intubation time decreased by 34% (92.6 to 60.8 sec) over the 5 attempts in mannequins. The success rate ranged from 84.6% to 90.8% and did not differ significantly over the 5 attempts.

Syed, T. H., Lakshmi, V., Paleologos, E., Lohmann, D., Mitchell, K., & Famiglietti, J. S. (2004). Analysis of process controls in land surface hydrological cycle over the continental United States. Journal of Geophysical Research: Atmospheres, 109(D22)., The paper uses two years (1997–1999) of data from the North American Land Data Assimilation System at National Centers for Environmental Prediction to analyze the variability of physical variables contributing to the hydrological cycle over the conterminous United States. The five hydrological variables considered in this study are precipitation, top layer soil moisture (0–10 cm), total soil moisture (0–200 cm), runoff, and potential evaporation. There are two specific analyses carried out in this paper. In the first case the principal components of the hydrological cycle are examined with respect to the loadings of the individual variables. This helps to ascertain the contribution of physical variables to the hydrological process in decreasing order of process importance. The results from this part of the study had revealed that both in annual and seasonal timescales the first two principal components account for 70–80% of the variance and that precipitation dominated the first principal component, the most dominant mode of spatial variability. It was followed by the potential evaporation as the secondmost dominant process controlling the spatial variability of the hydrologic cycle over the continental United States. In the second case each hydrological variable was examined individually to determine the temporal evolution of its spatial variability. The results showed the presence of heterogeneity in the spatial variability of hydrologic variables and the way these patterns of variance change with time. It has also been found that the temporal evolution of the spatial patterns did not resemble white noise; the time series of the scores of the principal components showed proper cyclicity at seasonal to annual timescales. The northwestern and the southeastern parts of the United States had been found to have contributed significantly toward the overall variability of potential evaporation and soil moisture over the United States.