Showing papers in "Journal of Hydrometeorology in 2009"

A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System

Abstract: The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over the continents at diverse spatial resolutions. A revised land surface hydrology (H-TESSEL) is introduced in the ECMWF operational model to address shortcomings of the land surface scheme, specifically the lack of surface runoff and the choice of a global uniform soil texture. New infiltration and runoff schemes are introduced with a dependency on the soil texture and standard deviation of orography. A set of experiments in stand-alone mode is used to assess the improved prediction of soil moisture at the local scale against field site observations. Comparison with basin-scale water balance (BSWB) and Global Runoff Data Centre (GRDC) datasets indicates a consistently larger dynamical range of land water mass over large continental areas and an improved prediction of river runoff, while the effect on atmospheric...

An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data

Abstract: The last several years have seen the development of a number of new satellite-derived, globally complete, high-resolution precipitation products with a spatial resolution of at least 0.25° and a temporal resolution of at least 3-hourly. These products generally merge geostationary infrared data and polar-orbiting passive microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. The Program to Evaluate High Resolution Precipitation Products (PEHRPP) was established to evaluate and intercompare these datasets at a variety of spatial and temporal resolutions with the intent of guiding dataset developers and informing the user community regarding the error characteristics of the products. As part of this project, the authors have performed a subdaily intercomparison of five high-resolution datasets [Climate Prediction Center morphing (CMORPH) technique; Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA); Naval Re...

Precipitation, Recycling, and Land Memory: An Integrated Analysis

Abstract: A synthesis of several approaches to quantifying land–atmosphere interactions is presented. These approaches use data from observations or atmospheric reanalyses applied to atmospheric tracer models and stand-alone land surface schemes. None of these approaches relies on the results of general circulation model simulations. A high degree of correlation is found among these independent approaches, and constructed here is a composite assessment of global land–atmosphere feedback strength as a function of season. The composite combines the characteristics of persistence of soil moisture anomalies, strong soil moisture regulation of evaporation rates, and reinforcement of water cycle anomalies through recycling. The regions and seasons that have a strong composite signal predominate in both summer and winter monsoon regions in the period after the rainy season wanes. However, there are exceptions to this pattern, most notably over the Great Plains of North America and the Pampas/Pantanal of South Ame...

Influence of Rainfall Scenario Construction Methods on Runoff Projections

Abstract: The future rainfall series used to drive hydrological models in most climate change impact studies is informed by global climate models (GCMs). This paper compares future runoff projections in ∼11 000 0.25° grid cells across Australia from a daily rainfall–runoff model driven with future daily rainfall series obtained using three simple scaling methods, informed by 14 GCMs. In the constant scaling and daily scaling methods, the historical daily rainfall series is scaled by the relative difference between GCM simulations for the future and historical climates. The constant scaling method scales all the daily rainfall by the same factor, and the daily scaling method takes into account changes in the daily rainfall distribution by scaling the different daily rainfall amounts differently. In the daily translation method, the GCM future daily rainfall series is translated to a 0.25° gridcell rainfall series using the relationship established between the historical GCM-scale rainfall and 0.25° gridcell...

Rainfall and Water Resources Variability in Sub-Saharan Africa during the Twentieth Century

Abstract: River basin rainfall series and extensive river flow records are used to characterize and improve understanding of spatial and temporal variability in sub-Saharan African water resources during the last century. Nine major international river basins were chosen for examination primarily for their extensive, good quality flow records. A range of statistical descriptors highlight the substantial variability in rainfall and river flows [e.g., differences in rainfall (flows) of up to −14% (−51%) between 1931–60 and 1961–90 in West Africa], the marked regional differences, and the modest intraregional differences. On decadal time scales, sub-Saharan Africa exhibits drying across the Sahel after the early 1970s, relative stability punctuated by extreme wet years in East Africa, and periodic behavior underlying high interannual variability in southern Africa. Central Africa shows very modest decadal variability, with some similarities to the Sahel in the adjoining basins. No consistent signals in rainfa...

Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins

Abstract: This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund‐Potsdam‐Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations.

An Intercomparison of ERS-Scat and AMSR-E Soil Moisture Observations with Model Simulations over France

Abstract: This paper presents a study undertaken in preparation of the work leading up to the assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the land surface model (LSM) Interaction Soil Biosphere Atmosphere (ISBA) at Meteo-France. This study consists of an intercomparison experiment of different space-borne platforms providing surface soil moisture information [Advanced Microwave Scanning Radiometer for Earth Observing (AMSR-E) and European Remote Sensing Satellite Scatterometer (ERS-Scat)] with the reanalysis soil moisture predictions over France from the model suite of Systeme d’analyse fournissant des renseignements atmospheriques a la neige (SAFRAN), ISBA, and coupled model (MODCOU; SIM) of Meteo-France for the years of 2003–05. Both modeled and remotely sensed data are initially validated against in situ observations obtained at the experimental soil moisture monitoring site Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) in southwestern France. Two different ...

Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations

Abstract: Root zone soil moisture controls the land-atmosphere exchange of water and energy and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments we assimilate synthetic surface soil moisture observations into four different models (Catchment, Mosaic, Noah and CLM) using the Ensemble Kalman Filter. We demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Our experiments also suggest that (faced with unknown true subsurface physics) overestimating surface to root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Finally, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.

A Modeling and Observational Framework for Diagnosing Local Land–Atmosphere Coupling on Diurnal Time Scales

Abstract: Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America

Abstract: Thispaperdescribesacomprehensiveassessmentofanewhigh-resolution,gauge‐satellite-basedanalysisof daily precipitation over continental South America during 2004. This methodology is based on a combination ofadditiveandmultiplicativebiascorrectionschemestogetthelowestbiaswhencomparedwiththeobserved values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [TropicalRainfallMeasuringMission(TRMM)MultisatellitePrecipitationAnalysisreal-timealgorithm]and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite PrecipitationAnalysis,researchversion;andthecombinedschemeproposedinthispaper.Thesemethodologieswere tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appearstobe a suitabletoolto producereal-time, high-resolution,gauge-and satellite-based analysesofdaily precipitation over land in regional domains.

GRACE-Based Estimates of Terrestrial Freshwater Discharge from Basin to Continental Scales

Abstract: In this study, new estimates of monthly freshwater discharge from continents, drainage regions, and global land for the period of 2003‐05 are presented. The method uses observed terrestrial water storage change estimates from the Gravity Recovery and Climate Experiment (GRACE) and reanalysis-based atmospheric moisture divergence and precipitable water tendency in a coupled land‐atmosphere water mass balance. The estimates of freshwater discharge are analyzed within the context of global climate and compared with previously published estimates. Annual cycles of observed streamflow exhibit stronger correlations with the computed discharge compared to those with precipitation minus evapotranspiration (P 2 E) in several of the world’s largest river basins. The estimate presented herein of the mean monthly discharge from South America (;846 km 3 month 21 ) is the highest among the continents and that flowing into the Atlantic Ocean (;1382 km 3 month 21 ) is the highest among the drainage regions. The volume of global freshwater discharge estimated here is 30 354 6 1212 km 3 yr 21 . Monthly variations of global freshwater discharge peak between August and September and reach a minimum in February. Global freshwater discharge is also computed using a global ocean‐atmosphere mass balance in order to validate the land‐ atmosphere water balance estimates and as a measure of global water budget closure. Results show close proximity between the two estimates of global discharge at monthly (RMSE 5 329 km 3 month 21 ) and annual time scales (358 km 3 yr 21 ). Results and comparisons to observations indicate that the method shows important potential for global-scale monitoring of combined surface water and submarine groundwater discharge at near‐real time, as well as for contributing to contemporary global water balance studies and for constraining global hydrologic model simulations.

Improving the Numerical Solution of Soil Moisture-Based Richards Equation for Land Models with a Deep or Shallow Water Table

Abstract: The soil moisture–based Richards equation is widely used in land models for weather and climate studies, but its numerical solution using the mass-conservative scheme in the Community Land Model is found to be deficient when the water table is within the model domain. Furthermore, these deficiencies cannot be reduced by using a smaller grid spacing. The numerical errors are much smaller when the water table is below the model domain. These deficiencies were overlooked in the past, most likely because of the more dominant influence of the free drainage bottom boundary condition used by many land models. They are fixed here by explicitly subtracting the hydrostatic equilibrium soil moisture distribution from the Richards equation. This equilibrium distribution can be derived at each time step from a constant hydraulic (i.e., capillary plus gravitational) potential above the water table, representing a steady-state solution of the Richards equation. Furthermore, because the free drainage condition h...

Seasonal Predictability of European Discharge: NAO and Hydrological Response Time

Abstract: In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill...

Improving Satellite-Based Rainfall Accumulation Estimates Using Spaceborne Surface Soil Moisture Retrievals

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 aidofground-based raingauge observations Special emphasisis placed ondemonstrating that the approachcan be applied in continental areas lacking ground-based observations and/or long-term satellite data records

PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis

Abstract: Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks–Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component a...

Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast Australia

Abstract: This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman–Monteith equation [remote sensing evapotranspiration (ERS)] in a lumped conceptual daily rainfall–runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and ERS, and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both...

Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation

Abstract: A retrieval of available water fraction ( fAW) is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere–Land Exchange Inversion (ALEXI) model. Available water serves as a proxy for soil moisture conditions, where fAW can be converted to volumetric soil moisture through two soil texture dependents parameters—field capacity and permanent wilting point. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be largely dictated by soil moisture conditions, accommodates the retrieval of an average fAW over the surface to the rooting depth of the active vegetation. For this method, the fraction of actual to potential evapotranspiration ( fPET) is computed from an ALEXI estimate of latent heat flux and potential evapotranspiration (PET). The ALEXI-estimated fPET can be related to fAW in the soil profile. Four unique fPET to fAW relationships are proposed and validated against Oklahoma Meso...

Improving Estimates of Heavy and Extreme Precipitation Using Daily Records from European Rain Gauges

Abstract: The long-term variability in heavy precipitation characteristics over Europe for the period 1950–2000 is analyzed using high-quality daily records of rain gauge measurements from the European Climate Assessment (ECA) dataset. To improve the accuracy of heavy precipitation estimates, the authors suggest estimating the fractional contribution of very wet days to total precipitation from the probability distribution of daily precipitation than from the raw data, as it is adopted for the widely used R95tot precipitation index. This is feasible under the assumption that daily precipitation follows an analytical distribution like the gamma probability density function (PDF). The extended index R95tt based on the gamma PDF is compared to the classical R95tot index. The authors find that R95tt is more stable, especially when precipitation extremes are estimated from the limited number of wet days of seasonal and monthly time series. When annual daily time series are analyzed, linear trends in R95tt and R...

Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

Abstract: Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

New Vegetation Albedo Parameters and Global Fields of Soil Background Albedo Derived from MODIS for Use in a Climate Model

Abstract: New values are derived for snow-free albedo of five plant functional types (PFTs) and the soil/litter substrate from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board Terra and Aqua. The derived albedo values are used to provide and test an improved specification of surface albedo for the land surface scheme known as the Joint U.K. Land Environment Simulator (JULES) that forms part of the Hadley Centre Global Environmental Model (HadGEM) climate model. The International Geosphere–Biosphere Programme (IGBP) global land cover map is used in combination with the MODIS albedo to estimate the albedo of each cover type in the IGBP classification scheme, from which the albedo values of the JULES PFTs are computed. The albedo of the soil/litter substrate, referred to as the soil background albedo, is derived from partially vegetated regions using a method that separates the vegetation contribution to the albedo signal from that of the soil/litter substrate. The g...

Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Measurement: A Case Study Evaluation over the Southwestern United States

Abstract: Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high tempora...

ASCAT Soil Moisture: An Assessment of the Data Quality and Consistency with the ERS Scatterometer Heritage

Abstract: This article presents a first comparison between remotely sensed surface soil moisture retrieved with the European Remote Sensing Satellite-2 (ERS-2) scatterometer (SCAT) and the corresponding product provided by the Advanced Scatterometer (ASCAT) on board Meteorological Operation satellite (MetOp), the first of a series of three satellites providing, among other things, continuity of global soil moisture observations using active microwave techniques for the next 15 yr. Three months of collocated 2007 data were used from the SCAT and ASCAT, limited to two study regions with different land cover composition. The result of the assessment is satisfactory and ensures consistency of migrating soil moisture retrieval from the long-term SCAT dataset to ASCAT measurements. The influence of a shift of observation incidence angle ranges between the two instrument generations was not found to be significant for the soil moisture retrieval. The correlation coefficients (R) between two relative soil moisture...

Regional Groundwater Evapotranspiration in Illinois

Abstract: The role of shallow unconfined aquifers in supplying water for evapotranspiration (ie, groundwater evaporation) is investigated in this paper Recent results from regional land surface modeling have indicated that in shallow water table areas, a large portion of evapotranspiration comes directly from aquifers However, little field evidence at the regional scale has been reported to support this finding Using a comprehensive 19-yr (1984‐2002) monthly hydrological dataset on soil moisture, water table depth, and streamflow in Illinois, regional recharge to and evaporation from groundwater are estimated by using soil water balance computation The 19-yr mean groundwater recharge is estimated to be 244 mm yr 21 (25% of precipitation), with uncertainty ranging from 202 to 278 mm yr 21 During the summer, the upward capillary flux from the shallow aquifer helps to maintain a high rate of evapotranspiration Groundwater evaporation (negative groundwater recharge) occurs during the period of July‐September, with a total of 314 mm (10% of evapotranspiration) Analysis of the relative soil saturation at 11 depths from 0 to 2 m deep supports the dominance of groundwater evaporation across the water table in dry periods The zero-flux plane separating the recharge zone from the evapotranspiration zone propagates downward from about 70- to 110-cm depth during summer, reflecting the water supply from progressively lower layers for evapotranspiration Despite its small magnitude, neglecting regional groundwater evaporation in shallow groundwater areas would result in underestimated root-zone soil moisture and hence evapotranspiration by as large as 20% in the dry summer seasons

Soil Moisture Feedbacks on Convection Triggers: The Role of Soil-Plant Hydrodynamics

Abstract: The linkages between soil moisture dynamics and convection triggers, defined here as the first crossing between the boundary layer height (hBL) and lifting condensation level (hLCL), are complicated by a large number of interacting processes occurring over a wide range of space and time scales. To progress on this problem, a soil–plant hydrodynamics model was coupled to a simplified ABL budget to explore the feedback of soil moisture on convection triggers. The soil–plant hydraulics formulation accounted mechanistically for features such as root water uptake, root water redistribution, and midday stomatal closure, all known to affect diurnal cycles of surface fluxes and, consequently, ABL growth. The ABL model considered the convective boundary layer as a slab with a discontinuity at the inversion layer. The model was parameterized using the wealth of data already collected for a maturing Loblolly pine plantation situated in the southeastern United States. A 30-day dry-down simulation was used to...

Northwest Territories and Nunavut Snow Characteristics from a Subarctic Traverse: Implications for Passive Microwave Remote Sensing

Abstract: During April 2007, a coordinated series of snow measurements was made across the Northwest Territories and Nunavut, Canada, during a snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. The purpose of the measurements was to document the general nature of the snowpack across this region for the evaluation of satellite- and model-derived estimates of snow water equivalent (SWE). Although detailed, local snow measurements have been made as part of ongoing studies at tundra field sites (e.g., Daring Lake and Trail Valley Creek in the Northwest Territories; Toolik Lake and the Kuparak River basin in Alaska), systematic measurements at the regional scale have not been previously collected across this region of northern Canada. The snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (<40 cm deep), usually with fewer than six layers. Where snow was deposited on lake and river ice, it was shallower, denser, ...

Correcting Unintended Perturbation Biases in Hydrologic Data Assimilation

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.

Validation of a dual-pass microwave land data assimilation system for estimating surface soil moisture in semiarid regions.

Abstract: This study examines the capability of a new microwave land data assimilation system (LDAS) for estimating soil moisture in semiarid regions, where soil moisture is very heterogeneous. This system assimilates the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) 6.9- and 18.7-GHz brightness temperatures into a land surface model (LSM), with a radiative transfer model as an observation operator. To reduce errors caused by uncertainties of system parameters, the LDAS uses a dual-pass assimilation algorithm, with a calibration pass to estimate major model parameters from satellite data and an assimilation pass to estimate the near-surface soil moisture. Validation data of soil moisture were collected in a Mongolian semiarid region. Results show that (i) the LDAS-estimated soil moistures are comparable to areal averages of in situ measurements, though the measured soil moistures were highly variable from site to site; (ii) the LSM-simulated soil moistures show less biases when the LSM uses LDAS-calibrated parameter values instead of default parameter values, indicating that the satellite-based calibration does contribute to soil moisture estimations; and (iii) compared to the LSM, the LDAS produces more robust and reliable soil moisture when forcing data become worse. The lower sensitivity of the LDAS output to precipitation is particularly encouraging for applying this system to regions where precipitation data are prone to errors.

Comparison of Precipitation Datasets over the Tropical South American and African Continents

Abstract: Six rainfall datasets are compared over the Amazon basin, Northeast Brazil, and the Congo basin. These datasets include three gauge-only precipitation products from the Climate Prediction Center (CPC), Global Precipitation Climatology Center (GPCC), and Brazilian Weather Forecast and Climate Studies Center (CLMNLS), and three combined gauge and satellite precipitation datasets from the CPC Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and Tropical Rainfall Measuring Mission (TRMM) product. The pattern of the annual precipitation is consistently represented by these data, despite the differences in methods and periods of averaging. Quantitatively, the differences in annual precipitation among these datasets are 5% more than the Amazon domain (08–158S, 508–708W), 22% more than Northeast Brazil (58–108S, 358–458W), and 11% more than the Congo domain (58N–108S, 158–308E). Over the Amazon domain the rainfall variation is well correlated between CPC, TRMM, GPCP, and GPCC (r 2 . 0.9) except for the northwestern Amazon, whereas CMAP and CLMNLS were different from these four datasets. Over the Congo basin, the coefficient of determination between these rainfall datasets is generally below 0.7. The empirical orthogonal functions analysis suggests large discrepancies in interannual and decadal variations of rainfall among these datasets, especially for the Congo basin and for the South American region after 1998. In general, CMAP, GPCC, TRMM, and GPCP significantly agree over the tropical areas in South America.

Synoptic Forcing of Precipitation over Greenland: Climatology for 1961–99

Abstract: Analysis of the synoptic climatology and precipitation patterns over the North Atlantic region allows for a better understanding of the atmospheric input to the mass balance of the Greenland ice sheet. The self-organizing map (SOM) technique was applied to the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) daily sea level pressure (SLP) data from 1961 to 1999 to objectively identify synoptic SLP patterns over the North Atlantic region. A total of 35 different SLP patterns were identified. Patterns common to the winter season are characterized by deep low pressure systems that approach Greenland through an active North Atlantic storm track, whereas patterns most common to the summer months are generally weaker and approach the ice sheet from the west through Baffin Bay. The blocking, splitting, and intensification of cyclones by the high elevations of the Greenland ice sheet were identified in this analysis. Analysis of ERA-40 precipitation associated with each SLP p...

Probing regional orographic controls of precipitation and cloudiness in the Central Andes using satellite data.

Abstract: Data obtained from NOAA’s Geostationary Operational Environmental Satellite (GOES) and NASA’s Tropical Rainfall Measuring Mission (TRMM) satellites were used to investigate the relationships between topography, large-scale circulation, and the climatology of precipitation and cloudiness in the Andes—specifically over Peru and the Altiplano Plateau—at diurnal, seasonal, and interannual time scales. The spatial variability of cloudiness was assessed through empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Results indicate that landform is the principal agent of the space–time variability of moist atmospheric processes in the Andes, with the first mode explaining up to 70% of all observed variability. These results substantiate the differences between “continental” (Andes and Himalayas) and “maritime” (Western Cordillera) orographic precipitation regimes, reflecting the degree to which upwind landmasses modulate moisture transport toward and across mountain barriers. GOE...