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Dongryeol Ryu, Wade T. Crow, Xiwu Zhan, and Thomas J. Jackson

Abstract

Hydrologic data assimilation has become an important tool for improving hydrologic model predictions by using observations from ground, aircraft, and satellite sensors. Among existing data assimilation methods, the ensemble Kalman filter (EnKF) provides a robust framework for optimally updating nonlinear model predictions using observations. In the EnKF, background prediction uncertainty is obtained using a Monte Carlo approach where state variables, parameters, and forcing data for the model are synthetically perturbed to explicitly simulate the error-prone representation of hydrologic processes in the model. However, it is shown here that, owing to the nonlinear nature of these processes, an ensemble of model forecasts perturbed by mean-zero Gaussian noise can produce biased background predictions. This ensemble perturbation bias in soil moisture states can lead to significant mass balance errors and degrade the performance of the EnKF analysis in deeper soil layers. Here, a simple method of bias correction is introduced in which such perturbation bias is corrected using an unperturbed model simulation run in parallel with the EnKF analysis. The proposed bias-correction scheme effectively removes biases in soil moisture and reduces soil water mass balance errors. The performance of the EnKF is improved in deeper layers when the filter is applied with the bias-correction scheme. The interplay of nonlinear hydrologic processes is discussed in the context of perturbation biases, and implications of the bias correction for real-data assimilation cases are presented.

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Anthony T. Cahill, Marc B. Parlange, Thomas J. Jackson, Peggy O’Neill, and T. J. Schmugge

Abstract

The use of remotely sensed near-surface soil moisture for the estimation of evaporation is investigated. Two widely used parameterizations of evaporation, the so-called α and β methods, which use near-surface soil moisture to reduce some measure of potential evaporation, are studied. The near-surface soil moisture is provided by a set of L- and S-band microwave radiometers, which were mounted 13 m above the surface. It is shown that soil moisture measured with a passive microwave sensor in combination with the β method yields reliable estimates of evaporation, whereas the α method is not as robust.

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S. A. Thorpe, T. R. Osborn, J. F. E. Jackson, A. J. Hall, and R. G. Lueck

Abstract

The rate of dissipation of turbulent kinetic energy has been measured with airfoil probes mounted on an autonomous vehicle, Autosub, on constant-depth legs at 2–10 m below the surface in winds up to 14 m s−1. The observations are mostly in an area limited by fetch to 26 km where the pycnocline depth is about 20 m. At the operational depths of 1.55–15.9 times the significant wave height H s, and in steady winds of about 11.6 m s−1 when the wave age is 11.7–17.2, dissipation is found to be lognormally distributed with a law-of-the-wall variation with depth and friction velocity. Breaking waves, leaving clouds of bubbles in the water, are detected ahead of the Autosub by a forward-pointing sidescan sonar, and the dissipation is measured when the clouds are subsequently reached. Bands of bubbles resulting from the presence of Langmuir circulation are identified by a semiobjective method that seeks continuity of band structure recognized by both forward- and sideways-pointing sidescan sonars. The times at which bands are crossed are determined and are used to relate dissipation rates and other measured parameters to the location of Langmuir bands. Shear-induced “temperature ramps” are identified with large horizontal temperature gradients. The turbulence measurements are consequently related to breaking waves, the bubble clouds, Langmuir circulation, and temperature ramps, and therefore to the principal processes of mixing in the near-surface layer of the ocean, all of which are found to have associated patterns of turbulent dissipation rates. A large proportion of the highest values of dissipation rate occur within bubble clouds. Dissipation is enhanced in the convergence region of Langmuir circulation at depths to about 10 m, and on the colder, bubble containing, side of temperature ramps associated with water advected downward from near the surface. Near the sea surface, turbulence is dominated by the breaking waves; below a depth of about 6H s the local vertical mixing in stronger Langmuir circulation cells exceeds that produced on average by the shear-induced eddies that form temperature ramps.

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Wade T. Crow, George J. Huffman, Rajat Bindlish, and Thomas J. Jackson

Abstract

Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records.

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Jackson Tan, George J. Huffman, David T. Bolvin, and Eric J. Nelkin

Abstract

As the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

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W. P. Kustas, T. J. Schmugge, K. S. Humes, T. J. Jackson, R. Parry, M. A. Weltz, and M. S. Moran

Abstract

Measurements of the microwave brightness temperature (TB) with the Pushbroom Microwave Radiometer (PBMR) over the Walnut Gulch Experimental Watershed were made on selected days during the MONSOON 90 field campaign. The PBMR is an L-band instrument (21-cm wavelength) that can provide estimates of near-surface soil moisture over a variety of surfaces. Aircraft observations in the visible and near-infrared wavelengths collected on selected days also were used to compute a vegetation index. Continuous micrometeorological measurements and daily soil moisture samples were obtained at eight locations during the experimental period. Two sites were instrumented with time domain reflectometry probes to monitor the soil moisture profile. The fraction of available energy used for evapotranspiration was computed by taking the ratio of latent heat flux (LE) to the sum of net radiation (Rn) and soil heat flux (G). This ratio is commonly called the evaporative fraction (EF) and normally varies between 0 and 1 under daytime convective conditions with minimal advection. A wide range of environmental conditions existed during the field campaign, resulting in average EF values for the study area varying from 0.4 to 0.8 and values of TB ranging from 220 to 280 K. Comparison between measured TB and EF for the eight locations showed an inverse relationship with a significant correlation (r 2 = 0.69). Other days were included in the analysis by estimating TB with the soil moisture data. Because transpiration from the vegetation is more strongly coupled to root zone soil moisture, significant scatter in this relationship existed at high values of TB or dry near-surface soil moisture conditions. It caused a substantial reduction in the correlation with r 2 = 0.40 or only 40% of the variation in EF being explained by TB. The variation in EF under dry near-surface soil moisture conditions was correlated to the amount of vegetation cover estimated with a remotely sensed vegetation index. These findings indicate that information obtained from optical and microwave data can be used for quantifying the energy balance of semiarid areas. The microwave data can indicate when soil evaporation is significantly contributing to EF, while the optical data is helpful for quantifying the spatial variation in EF due to the distribution of vegetation cover.

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Alan E. Lipton, George D. Modica, Scot T. Heckman, and Arthur J. Jackson

Abstract

A system for time-continuous mesoscale weather analysis is applied to a study of convective cloud development in central Florida. The analysis system incorporates water vapor concentrations and surface temperatures retrieved from infrared VISSR (Visible–Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS) satellite data, with coupling between the retrieval process and time integration of a mesoscale model. Analyses prepared with variations of this coupled system are compared with a control numerical analysis prepared with only conventional meteorological observations and are validated against surface and upper-air data collected for the Convection and Precipitation/Electrification experiment. The coupled analyses assimilate six sets of VAS data over an 8-h period on 19 July 1991 and depict water vapor gradients at far greater horizontal resolution than is available from conventional observations and with an overall accuracy better than the control analysis. The coupled system's ability to assimilate multiple sets of VAS data, with meteorological continuity provided by the model, was important to the accuracy and the breadth of coverage of the water vapor analysis amid changing cloud cover conditions. The surface temperature information provided by the VAS was neither harmful nor very helpful to the mesoscale analysis for this case, owing to the combination of mediocre satellite viewing conditions and the apparent low importance of land surface temperature gradients to the meteorology of the day. Convective stability parameters computed from the coupled analysis data at 1000 local time corresponded closely with patterns of cloud development in the early afternoon.

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H. Gao, E. F. Wood, T. J. Jackson, M. Drusch, and R. Bindlish

Abstract

Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

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Qing Liu, Rolf H. Reichle, Rajat Bindlish, Michael H. Cosh, Wade T. Crow, Richard de Jeu, Gabrielle J. M. De Lannoy, George J. Huffman, and Thomas J. Jackson

Abstract

The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.

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D. R. Jackson, A. Gadian, N. P. Hindley, L. Hoffmann, J. Hughes, J. King, T. Moffat-Griffin, A. C. Moss, A. N. Ross, S. B. Vosper, C. J. Wright, and N. J. Mitchell

Abstract

Gravity waves (GWs) play an important role in many atmospheric processes. However, the observation-based understanding of GWs is limited, and representing them in numerical models is difficult. Recent studies show that small islands can be intense sources of GWs, with climatologically significant effects on the atmospheric circulation. South Georgia, in the South Atlantic, is a notable source of such “small island” waves. GWs are usually too small scale to be resolved by current models, so their effects are represented approximately using resolved model fields (parameterization). However, the small-island waves are not well represented by such parameterizations, and the explicit representation of GWs in very-high-resolution models is still in its infancy. Steep islands such as South Georgia are also known to generate low-level wakes, affecting the flow hundreds of kilometers downwind. These wakes are also poorly represented in models.

We present results from the South Georgia Wave Experiment (SG-WEX) for 5 July 2015. Analysis of GWs from satellite observations is augmented by radiosonde observations made from South Georgia. Simulations were also made using high-resolution configurations of the Met Office Unified Model (UM). Comparison with observations indicates that the UM performs well for this case, with realistic representation of GW patterns and low-level wakes. Examination of a longer simulation period suggests that the wakes generally are well represented by the model. The realism of these simulations suggests they can be used to develop parameterizations for use at coarser model resolutions.

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