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Christa D. Peters-Lidard and Luke H. Davis

Abstract

During the Southern Great Plains 1997 Hydrology Experiment, a tethersonde system was deployed at the U.S. Department of Energy’s Atmospheric Radiation Measurement Cloud and Radiation Test Bed (ARM CART) central facility. Additional measurements included several surface flux stations at the central facility and radiosondes at the ARM CART central and boundary facilities. Combined, these data support an examination of regional flux estimates obtained via the atmospheric boundary layer conservation approach. Because the tethersonde was deployed successfully only under light to moderate wind conditions, the effects of advection on estimation of regional fluxes generally are found to be small. Consistent with previous studies, direct estimation of the sensible heat flux yields more accuracy than direct estimation of the latent heat flux. Use of available energy measured at surface flux stations along with the direct sensible heat flux estimates yields latent heat estimates of similar accuracy to those obtained for the sensible heat flux. It is observed that variability in the entrainment parameter exhibits a considerable diurnal cycle, presumably related to the interplay between buoyant and shear production of turbulent kinetic energy near the entrainment zone.

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Di Wu, Christa Peters-Lidard, Wei-Kuo Tao, and Walter Petersen

Abstract

The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real-time forecasts were conducted utilizing the NASA-Unified Weather Research and Forecasting (NU-WRF) Model to support the daily weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are conducted with different soil initializations, one from the spatially interpolated North American Mesoscale Forecast System (NAM) and the other produced by the Land Information System (LIS) using daily analysis of bias-corrected stage IV data. Both forecasts are then compared with NAM, stage IV, and Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) to understand the impact of land surface initialization on the predicted precipitation. In general, both NU-WRF runs are able to reproduce individual peaks of precipitation at the right time. NU-WRF is also able to replicate a better rainfall spatial distribution compared with NAM. Further sensitivity tests show that the high-resolution runs (1 and 3 km) are able to better capture the precipitation event compared to its coarser-resolution counterpart (9 km). Finally, the two sets of NU-WRF simulations produce very close rainfall characteristics in bias, spatial and temporal correlation scores, and probability density function. The land surface initialization does not show a significant impact on short-term rainfall forecast, which is largely because of high soil moisture during the field campaign period.

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Sarah Ringerud, Christa Peters-Lidard, Joe Munchak, and Yalei You

Abstract

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

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Yudong Tian, Christa D. Peters-Lidard, and John B. Eylander

Abstract

A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.

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Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

Abstract

Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

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Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, and Yudong Tian

Abstract

The downwelling shortwave radiation on the earth’s land surface is affected by the terrain characteristics of slope and aspect. These adjustments, in turn, impact the evolution of snow over such terrain. This article presents a multiscale evaluation of the impact of terrain-based adjustments to incident shortwave radiation on snow simulations over two midlatitude regions using two versions of the Noah land surface model (LSM). The evaluation is performed by comparing the snow cover simulations against the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The model simulations are evaluated using categorical measures, such as the probability of detection of “yes” events (PODy), which measure the fraction of snow cover presence that was correctly simulated, and false alarm ratio (FAR), which measures the fraction of no-snow events that was incorrectly simulated. The results indicate that the terrain-based correction of radiation leads to systematic improvements in the snow cover estimates in both domains and in both LSM versions (with roughly 12% overall improvement in PODy and 5% improvement in FAR), with larger improvements observed during snow accumulation and melt periods. Increased contribution to PODy and FAR improvements is observed over the north- and south-facing slopes, when the overall improvements are stratified to the four cardinal aspect categories. A two-dimensional discrete Haar wavelet analysis for the two study areas indicates that the PODy improvements in snow cover estimation drop to below 10% at scales coarser than 16 km, whereas the FAR improvements are below 10% at scales coarser than 4 km.

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Amin K. Dezfuli, Benjamin F. Zaitchik, Hamada S. Badr, Jason Evans, and Christa D. Peters-Lidard

Abstract

Rainfall variability in the Tigris–Euphrates headwaters is a result of interaction between topography and meteorological features at a range of spatial scales. Here, the Weather Research and Forecasting (WRF) Model, driven by the NCEP–DOE AMIP-II reanalysis (R-2), has been implemented to better understand these interactions. Simulations were performed over a domain covering most of the Middle East. The extended simulation period (1983–2013) enables us to study seasonality, interannual variability, spatial variability, and extreme events of rainfall. Results showed that the annual cycle of precipitation produced by WRF agrees much more closely with observations than does R-2. This was particularly evident during the transition months of April and October, which were further examined to study the underlying physical mechanisms. In both months, WRF improves representation of interannual variability relative to R-2, with a substantially larger benefit in April. This improvement results primarily from WRF’s ability to resolve two low-level, terrain-induced flows in the region that are either absent or weak in R-2: one parallel to the western edge of the Zagros Mountains, and one along the east Turkish highlands. The first shows a complete reversal in its direction during wet and dry days: when flowing southeasterly it transports moisture from the Persian Gulf to the region, and when flowing northwesterly it blocks moisture and transports it away from the region. The second is more directly related to synoptic-scale systems and carries moist, warm air from the Mediterranean and Red Seas toward the region. The combined contribution of these flows explains about 50% of interannual variability in both WRF and observations for April and October precipitation.

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Sujay V. Kumar, Christa D. Peters-Lidard, Kristi R. Arsenault, Augusto Getirana, David Mocko, and Yuqiong Liu

Abstract

Accurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvements in streamflow estimates, especially over the western United States, the upper Missouri River, and parts of the Northeast and upper Mississippi River. This study thus demonstrates a simple approach for exploiting the information from SCA observations in data assimilation.

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David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

Abstract

This study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation towards improved simulation of agricultural drought.

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Kristi R. Arsenault, Grey S. Nearing, Shugong Wang, Soni Yatheendradas, and Christa D. Peters-Lidard

Abstract

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.

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