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Xiaogang Gao and Soroosh Sorooshian

Abstract

In the surface hydrologic pararmeterization of general circulation models (GCMs), it is commonly assumed that the precipitation processes are homogeneous over a GCM grid square and that the precipitation intensity is uniformly distributed. Based on evidence that the spatial distribution of precipitation within a GCM grid square is crucial for the land surface hydrology parameterization, a few researchers have explored the impacts of assuming that the precipitation is exponentially distributed. This paper explores the suitability of the afore-mentioned assumptions. First, a statistical analysis is conducted of historical precipitation data for three GCM grids in different regions of the United States. The analysis suggests that neither the uniform nor the exponential distribution assumption may be suitable at the GCM grid scale and, that instead, the spatial variability in precipitation is characterized by statistical patterns that are inhomogeneous. These patterns vary from grid to grid and are induced by the interaction between atmospheric conditions and various land surface characteristics, such as topographical features, surface properties, etc. Within the same grid square, however, the statistical patterns are generally constant from year to year. Based on this analysis, a computationally viable (i.e., usable with GCMs) stochastic precipitation disaggregation scheme that utilizes these stable statistical patterns is proposed. The method was used to generate spatially distributed hourly rainfall for a summer season in the southwestern region of the continental United States. Analysis of the results shows that the methodology preserves the seasonal characteristics of spatial variability in precipitation that is observed in the long-term historical data.

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Edwin Welles and Soroosh Sorooshian

Abstract

One element of a complete verification system is the ability to determine why forecasts behave as they do. This paper describes and demonstrates an operationally feasible method for conducting this type of diagnostic verification analysis. Hindcasts are generated using different configurations of the forecast system and then the skill of the generated hindcasts is compared. The hindcasts and comparisons are constructed to isolate individual elements of the forecast process. The approach is used to evaluate the role of model calibration, model initial conditions, and precipitation forecasts in generating skill for deterministic river forecasts. The authors find that calibration and initial conditions provide skill for the short lead-time forecasts, with precipitation forecasts providing the majority of the skill in forecasts of high stages at longer lead times. At all lead times, this study shows model calibration is essential, as the calibration makes forecasts reliable.

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Thomas Pagano, David Garen, and Soroosh Sorooshian

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An analysis was conducted of almost 5000 operational seasonal streamflow forecast errors across the western United States. These forecasts are for 29 unregulated rivers with diversity in geography and climate. Deterministic evaluations revealed strong correspondence between observations and forecasts issued 1 April. Forecasts issued earlier in the season were more uncertain yet remained skillful. The average change in forecast performance between January and April was primarily linked to the climatological seasonal cycle of precipitation: regions with climatologically wet winters and dry springs (e.g., California) showed much more forecast improvement between January and April than did regions with dry winters and wet springs (e.g., western Great Plains, Colorado Front Range). Other climatological factors played a secondary role; for example, mixed rain–snow basins in the Pacific Northwest did not show as significant an improvement in skill versus lead time as might otherwise be expected. Mixed trends in 1 April forecast skill were noted since the 1980s, with increased skill in California and Nevada, and a decline in skill in the Colorado River basin. Increased variability in streamflow was also noted across most of the western United States, although this did not appear to be the only factor responsible for trends in forecast skill.

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Hao Liu, Soroosh Sorooshian, and Xiaogang Gao

Abstract

Studies have been reported about the efficacy of satellites for measuring precipitation and about quantifying their errors. Based on these studies, the errors are associated with a number of factors, among them, intensity, location, climate, and season of the year. Several error models have been proposed to assess the relationship between the error and the rainfall intensity. However, it is unknown whether these models are adaptive to different seasons, different regions, or different types of satellite-based estimates. Therefore, how the error–intensity relationship varies with the season or region is unclear. To investigate these issues, a parametric joint pdf model is proposed to analyze and study the 9-yr satellite-derived precipitation datasets of Climate Prediction Center (CPC) morphing technique (CMORPH); PERSIANN; and the real-time TRMM product 3B42, version 7 (TRMM-3B42-RTV7). The NEXRAD Stage IV product is the ground reference. The adaptability of the proposed model is verified by applying it to three locations (Oklahoma, Montana, and Florida) and by applying it to cold season, warm season, and the entire year. Then, the heteroscedasticities in the errors of satellite-based precipitation measurements are investigated using the proposed model under those scenarios. The results show that the joint pdfs have the same formulation under these scenarios, whereas their parameter sets were adaptively adjusted. This parametric model reveals detailed information about the spatial and seasonal variations of the satellite-based precipitation measurements. It is found that the shape of the conditional pdf shifts across the intensity ranges. At the ~10–20 mm day−1 range, the conditional pdf is L shaped, while at the ~40–60 mm day−1 range, it becomes more bell shaped. It is also concluded that no single satellite-based precipitation product outperforms others with respect to the different scenarios (i.e., seasons, regions, and climates).

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Scott Lee Sellars, Xiaogang Gao, and Soroosh Sorooshian

Abstract

This manuscript introduces a novel computational science approach for studying the impact of climate variability on precipitation. The approach uses an object-oriented connectivity algorithm that segments gridded near-global satellite precipitation data into four-dimensional (4D) objects (longitude, latitude, time, and intensity). These precipitation systems have distinct spatiotemporal properties that are counted, tracked, described, and stored in a searchable database. A case study of western United States precipitation systems is performed, demonstrating the unique properties and capabilities of this object-oriented database. The precipitation dataset used in the case study is the University of California, Irvine, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) from 1 March 2000 to 1 January 2011. A search of the database for all western United States precipitation systems during this time period returns 626 precipitation systems as objects. By analyzing these systems as segmented objects, joint interactions of the selected climate phenomena 1) Arctic Oscillation (AO), 2) Madden–Julian oscillation (MJO), and 3) El Niño–Southern Oscillation (ENSO) on precipitation can be shown. They directly show the increased/decreased likelihood of having precipitation systems occurring over the western United States (monthly count) during phases of these climate phenomena. It is found that specific climate phenomena impact the monthly count of the events differently, and that the joint interaction of climate phenomena of the AO–MJO and AO–ENSO is important, especially during certain months of the year. It is also found that these interactions impact the physical features of the precipitation systems themselves.

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Nasrin Nasrollahi, Kuolin Hsu, and Soroosh Sorooshian

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical characteristics and therefore precipitation retrievals. Additionally, CloudSat, selected as a NASA Earth Sciences Systems Pathfinder satellite mission, is equipped with a 94-GHz radar that can detect the occurrence of surface rainfall. The CloudSat radar flies in formation with Aqua with only an average of 60 s delay. The availability of surface rain presence based on CloudSat together with the multispectral capabilities of MODIS makes it possible to create a training dataset to distinguish false rain areas based on their radiances in satellite precipitation products [e.g., Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)]. The brightness temperatures of six MODIS water vapor and infrared channels are used in this study along with surface rain information from CloudSat to train an artificial neural network model for no-rain recognition. The results suggest a significant improvement in detecting nonprecipitating regions and reducing false identification of precipitation. Also, the results of the case studies of precipitation events during the summer and winter of 2007 over the United States show an accuracy of 77% no-rain identification and 93% detection accuracy, respectively.

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Sepideh Sarachi, Kuo-lin Hsu, and Soroosh Sorooshian

Abstract

Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5° × 5° over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used to represent the joint probability distribution of satellite (PERSIANN) and radar (stage IV) rainfall. The method is further investigated for the period of 2006–08 over the Illinois watershed south of Siloam Springs, Arkansas. Results show that, using the proposed method, the estimation of the precipitation is improved in terms of percent bias and root-mean-square error.

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Tim Bellerby, Kuo-lin Hsu, and Soroosh Sorooshian

Abstract

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (∼4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear.

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Kristie J. Franz, Terri S. Hogue, and Soroosh Sorooshian

Abstract

Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow–Atmosphere–Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service’s (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2–4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3–4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating.

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Efrat Morin, Robert A. Maddox, David C. Goodrich, and Soroosh Sorooshian

Abstract

Radar-based estimates of rainfall rates and accumulations are one of the principal tools used by the National Weather Service (NWS) to identify areas of extreme precipitation that could lead to flooding. Radar-based rainfall estimates have been compared to gauge observations for 13 convective storm events over a densely instrumented, experimental watershed to derive an accurate reflectivity–rainfall rate (i.e., ZR) relationship for these events. The resultant ZR relationship, which is much different than the NWS operational ZR, has been examined for a separate, independent event that occurred over a different location. For all events studied, the NWS operational ZR significantly overestimates rainfall compared to gauge measurements. The gauge data from the experimental network, the NWS operational rain estimates, and the improved estimates resulting from this study have been input into a hydrologic model to “predict” watershed runoff for an intense event. Rainfall data from the gauges and from the derived ZR relation produce predictions in relatively good agreement with observed streamflows. The NWS ZR estimates lead to predicted peak discharge rates that are more than twice as large as the observed discharges. These results were consistent over a relatively wide range of subwatershed areas (4–148 km2). The experimentally derived Z–R relationship may provide more accurate radar estimates for convective storms over the southwest United States than does the operational convective ZR used by the NWS. These initial results suggest that the generic NWS ZR relation, used nationally for convective storms, might be substantially improved for regional application.

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