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  • Author or Editor: Rafael L. Bras x
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Marcos L. Pessoa
,
Rafael L. Bras
, and
Earle R. Williams

Abstract

Weather radar, in combination with a distributed rainfall-runoff model, promises to significantly improve real-time flood forecasting. This paper investigates the value of radar-derived precipitation in forecasting streamflow in the Sieve River basin, near Florence, Italy. The basin is modeled with a distributed rainfall-runoff model that exploits topographic information available from digital elevation maps. The sensitivity of the flood forecast to various properties of the radar-derived rainfall is studied. It is found that use of the proper radar reflectivity-rainfall intensity (Z-R) relationship is the most crucial factor in obtaining correct food hydrographs. Errors resulting from spatially averaging radar rainfall are acceptable, but the use of discrete point information (i.e., raingage) can lead to serious problems. Reducing the resolution of the 5-min radar signal by temporally averaging over 15 and 30 min does not lead to major errors. Using 3-bit radar data (rather than the usual 8-bit data) to represent intensifies results in significant operational savings without serious problems in hydrograph accuracy.

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William B. Bennett
,
Jingfeng Wang
, and
Rafael L. Bras

Abstract

This study investigates the use of a previously published algorithm for estimating ground heat flux (GHF) at the global scale. The method is based on an analytical solution of the diffusion equation for heat transfer in a soil layer and has been shown to be effective at the point scale. The algorithm has several advantageous properties: 1) it only needs a single-level input of surface (skin) temperature, 2) the time-mean GHF can be derived directly from time-mean skin temperature, 3) it has reduced sensitivity to the variability in soil thermal properties and moisture, 4) it does not requires snow depth, and 5) it is computationally effective. A global map of the necessary thermal inertia parameter is derived using reanalysis data as a function of soil type. These parameter estimates are comparable to values obtained from in situ observations. The new global GHF estimates are generally consistent with the reanalysis GHF output simulated using two-layer soil hydrology models. The authors argue that the new algorithm is more robust and trustworthy in regions where they differ. The proposed algorithm offers potential benefits for direct assimilation of observations of surface temperature as well as GHF into the reanalysis models at various time scales.

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Ardeshir M. Ebtehaj
,
Rafael L. Bras
, and
Efi Foufoula-Georgiou

Abstract

Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.

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Jiaying Zhang
,
Liao-Fan Lin
, and
Rafael L. Bras

Abstract

Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.

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Jingfeng Wang
,
Elfatih A. B. Eltahir
, and
Rafael L. Bras

Abstract

Mesoscale circulations forced by a random distribution of surface sensible heat flux have been investigated using a three-dimensional numerical model. The complex land surface is modeled as a homogeneous random field characterized by a spectral distribution. Standard deviation and length scale of the sensible heat flux at the surface have been identified as the important parameters that describe the thermal variability of land surface. The form of the covariance of the random surface forcing is not critical in driving the mesoscale circulation. The thermally induced mesoscale circulation is significant and extends up to about 5 km when the atmosphere is neutral. It becomes weak and is suppressed when the atmosphere is stable. The mesoscale momentum flux is much stronger than the corresponding turbulent momentum flux in the neutral atmosphere, while the two are comparable in the stable atmosphere. The mesoscale heat flux has a different vertical profile than turbulent heat flux and may provide a major heat transport mechanism beyond the planetary boundary layer. The impact of synoptic wind on the mesoscale circulations is relatively weak. Nonlinear advection terms are responsible for momentum flux in the absence of synoptic wind.

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Shafiqul Islam
,
Rafael L. Bras
, and
Kerry A. Emanuel

Abstract

A general framework has been developed to study the predictability of space–time averages of mesoscale rainfall in the tropics. A comparative ratio between the natural variability of the rainfall process and the prediction error is used to define the predictability range. The predictability of the spatial distribution of precipitation is quantified by the cross correlation between the control and the perturbed rainfall fields. An upper limit of prediction error, called normalized variability, has been derived as a function of space–time averaging. Irrespective of the type and amplitude of perturbations, a space–time averaging set of 25 km2–15 min (or larger time averaging) is found to be necessary to limit the error growth up to or below the prescribed large-scale mean rainfall.

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Fabio Castelli
,
Rafael L. Bras
, and
Kerry A. Emanuel

Abstract

The existence of analytical solutions for two-dimensional nonlinear semigeostrophic models of moist frontogenesis is investigated. Two different schemes for the modeling of stratiform cloud thermodynamics are taken into account, one based on the assumption of an everywhere cloudy environment, while in the other the atmosphere is considered to be exactly saturated and only condensation effects are relevant. In the first case, an exact analytical solution is derived for arbitrary boundary conditions, which satisfies the requirements for the validation of the semigeostrophic approximation even when the atmosphere is conditionally unstable with respect to slantwise convection. The growth of symmetric instabilities with no short-wave cutoff is predicted for this conditionally unstable atmosphere, even under the approximations of the semigeostrophic theory. An analogous, but approximate, analytical solution is then proposed for the second case. The errors introduced by the approximation are, however, not bigger than the terms neglected in the semigeostrophic approximation itself.

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Jingfeng Wang
,
Rafael L. Bras
, and
Elfatih A. B. Eltahir

Abstract

A numerical mesoscale model has been used to investigate the impact of mesoscale circulations on the distribution of precipitation and cloudiness over a deforested area in Amazonia. Observed patterns of deforestation in Rondônia, Amazonia, with scales on the order of 10 km were used in this study to describe land surface conditions. Various simulations have been performed to identify the conditions under which the mesoscale circulations induced by the heterogeneous land surface could enhance cloudiness and local rainfall. The simulation results suggest that the synoptic forcing, in terms of atmospheric stability and background horizontal wind, dominates during the rainy season; synoptic conditions were so favorable to moist convection that the added effect of surface heterogeneity was negligible. During the dry season, a noticeable impact of mesoscale circulations resulting in enhancement of shallow clouds was simulated; the mesoscale circulations also triggered scattered deep convection that altered the spatial distribution of precipitation. During the break period, the transition from the rainy season to the dry season, the impact of mesoscale circulations on low-level clouds was evident only after reducing the magnitude of the background wind.

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Liao-Fan Lin
,
Ardeshir M. Ebtehaj
,
Alejandro N. Flores
,
Satish Bastola
, and
Rafael L. Bras

Abstract

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

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Liao-Fan Lin
,
Ardeshir M. Ebtehaj
,
Rafael L. Bras
,
Alejandro N. Flores
, and
Jingfeng Wang

Abstract

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

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