Assimilating along-track SLA data using the EnOI in an eddy resolving model of the Agulhas system

The greater Agulhas Current is one of the most energetic current systems in the global ocean. It plays a fundamental role in determining the mean state and variability of the regional marine environment, affecting its resources and ecosystem, the regional weather and the global climate on a broad range of temporal and spatial scales. In the absence of a coherent in-situ and satellite-based observing system in the region, modelling and data assimilation techniques play a crucial role in both furthering the quantitative understanding and providing better forecasts of this complicated western boundary current system. In this study, we use a regional implementation of the Hybrid Coordinate Ocean Model and assimilate along-track satellite sea level anomaly (SLA) data using the Ensemble Optimal Interpolation (EnOI) data assimilation scheme. This study lays the foundation towards the development of a regional prediction system for the greater Agulhas Current system. Comparisons to independent in-situ drifter observations show that data assimilation reduces the error compared to a free model run over a 2-year period. Mesoscale features are placed in more consistent agreement with the drifter trajectories and surface velocity errors are reduced. While the model-based forecasts of surface velocities are not as accurate as persistence forecasts derived from satellite altimeter observations, the error calculated from the drifter measurements for eddy kinetic energy is significantly lower in the assimilation system compared to the persistence forecast. While the assimilation of along-track SLA data introduces a small bias in sea surface temperatures, the representation of water mass properties and deep current velocities in the Agulhas system is improved.     

Emerging Technologies and Synergies for Airborne and Space-Based Measurements of Water Vapor Profiles

A deeper understanding of how clouds will respond to a warming climate is one of the outstanding challenges in climate science. Uncertainties in the response of clouds, and particularly shallow clouds, have been identified as the dominant source of the discrepancy in model estimates of equilibrium climate sensitivity. As the community gains a deeper understanding of the many processes involved, there is a growing appreciation of the critical role played by fluctuations in water vapor and the coupling of water vapor and atmospheric circulations. Reduction of uncertainties in cloud-climate feedbacks and convection initiation as well as improved understanding of processes governing these effects will result from profiling of water vapor in the lower troposphere with improved accuracy and vertical resolution compared to existing airborne and space-based measurements. This paper highlights new technologies and improved measurement approaches for measuring lower tropospheric water vapor and their expected added value to current observations. Those include differential absorption lidar and radar, microwave occultation between low-Earth orbiters, and hyperspectral microwave remote sensing. Each methodology is briefly explained, and measurement capabilities as well as the current technological readiness for aircraft and satellite implementation are specified. Potential synergies between the technologies are discussed, actual examples hereof are given, and future perspectives are explored. Based on technical maturity and the foreseen near-mid-term development path of the various discussed measurement approaches, we find that improved measurements of water vapor throughout the troposphere would greatly benefit from the combination of differential absorption lidar focusing on the lower troposphere with passive remote sensors constraining the upper-tropospheric humidity.     

A Study on the Impact of Observation Assimilation on the Numerical Simulation of Tropical Cyclones JAL and THANE Using 3DVAR

In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72 h predictions, respectively, for the two cases using assimilation of all observations. Simulated rainfall estimates indicate that while the assimilation of scatterometer wind data improves the location of the rainfall, the assimilation of MODIS profiles produces a realistic pattern and amount of rainfall, close to the observational estimates.     

GNSS掩星中大气水汽的非线性反演

研究利用无线电掩星探测数据反演大气水汽参数,文中使用一维非线性方法,反演对流层水汽压廓线.首先,定义了一维目标函数,并以几何光学假设为前提,反演出电波弯曲角;将电波弯曲角作为掩星探测量,大气模式输出的温、湿参量作为初始场,代入目标函数,对目标函数求最优反演出水汽压廓线.文中给出了部分反演结果,经讨论分析认为:非线性方法反演水汽压对初始场的精度依赖较小,能够同时反演出大气温度\水汽压廓线及相应的误差分析,对数值天气预报的应用及数据同化研究具有重要意义.     

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today’s optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf’s initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.     

A comparison of space-based observations of the energy budgets of the mesosphere and the troposphere

The flow of energy from the Sun, through the atmosphere, to the Earth's surface and oceans, and ultimately back to space, controls the weather and climate of the planet. Since the dawn of the Space Age, the energy balance of the planet has been measured by orbiting satellites. Over the past 40 years the technology and the scientific understanding have developed to the point where we can measure not only the energy balance of the entire Earth and its atmosphere but also of the various regions of the atmosphere including the troposphere, the stratosphere, and the mesosphere. With the planned space-based observations of the sources and sinks of energy in the mesosphere, the energy balance of this region of the atmosphere may soon be better understood from observations than the energy balance of the troposphere on seasonal to annual timescales. Fundamental to this assertion is the fact that the primary sources and sinks of radiative and chemical potential energy, the thermal structure, and the winds in the mesosphere are to be directly observed by space-based instrumentation at high vertical resolution, in contrast to the troposphere. In this paper we review some of the planned measurements of the energy budgets of the atmosphere from existing and future space-based platforms. We particularly show how the airglow can be used to determine many of the key sources of energy in the mesosphere. These ideas provide the basis for interpretation of new space-based measurements of the mesosphere planned in the near future.     

The Impact of Vertical Resolution in the Explicit Numerical Forecasting of Radiation Fog: A Case Study

Numerical experiments are performed with a comprehensive one-dimensional boundary layer/fog model to assess the impact of vertical resolution on explicit model forecasts of an observed fog layer. Two simulations were performed, one using a very high resolution and another with a vertical grid typical of current high-resolution mesoscale models. Both simulations were initialized with the same profiles, derived from observations from a fog field experiment. Significant differences in the onset and evolution of fog were found. The results obtained with the high-resolution simulation are in overall better agreement with available observations. The cooling rate before the appearance of fog is better represented, while the evolution of the liquid water content within the fog layer is more realistic. Fog formation is delayed in the low resolution simulation, and the water content in the fog layer shows large-amplitude oscillations. These results show that the numerical representation of key thermo-dynamical processes occurring in fog layers is significantly altered by the use of a grid with reduced vertical resolution.     

A Study on the Performance of the NCMRWF Analysis and Forecasting System During Asian Summer Monsoon: Thermodynamic Aspects

—The thermodynamic characteristics of the Asian summer monsoon are examined with a global analysis-forecast system. In this study, we investigated the large-scale balances of heat and moisture by making use of operational analyses as well as forecast fields for June, July and August (JJA), 1994. Apart from elucidating systematic errors in the temperature and moisture fields, the study expounds the influence of these errors on the large-scale budgets of heat and moisture over the monsoon region. The temperature forecasts of the model delineate predominant cooling in the middle and lower tropospheres over the monsoon region. Similarly, the moisture forecasts evince a drying tendency in the lower troposphere. However, certain sectors of moderate moistening exist over the peninsular India and adjoining oceanic sectors of the Arabian Sea and Bay of Bengal.¶The broad features of the large-scale heat and moisture budgets represented by the analysis/forecast fields indicate good agreement with the observed aspects of the summer monsoon circulation. The model forecasts fail to retain the analyzed atmospheric variability in terms of the mean circulation, which is indicated by underestimation of various terms of heat and moisture budgets with an increase in the forecast period. Further, the forecasts depict an anomalous diabatic cooling layer in the lower middle troposphere of the monsoon region which inhibits vertical transfer of heat and moisture from the mixed layer of the atmospheric boundary layer to the middle troposphere. In effect, the monsoon circulation is considerably weakened with an increase in the forecast period. The treatment of shallow convection and the use of interactive clouds in the model can reduce the cooling bias considerably.     

Requirements of a global near-surface soil moisture satellite mission: accuracy,repeat time,and spatial resolution

Soil moisture satellite mission accuracy, repeat time and spatial resolution requirements are addressed through a numerical twin data assimilation study. Simulated soil moisture profile retrievals were made by assimilating near-surface soil moisture observations with various accuracy (0, 1, 2, 3, 4, 5 and 10%v/v standard deviation) repeat time (1, 2, 3, 5, 10, 15, 20 and 30 days), and spatial resolution (0.5, 6, 12 18, 30, 60 and 120 arc-min). This study found that near-surface soil moisture observation error must be less than the model forecast error required for a specific application when used as data assimilation input, else slight model forecast degradation may result. It also found that near-surface soil moisture observations must have an accuracy better than 5%v/v to positively impact soil moisture forecasts, and that daily near-surface soil moisture observations achieved the best soil moisture and evapotranspiration forecasts for the repeat times assessed, with 1–5 day repeat times having the greatest impact. Near-surface soil moisture observations with a spatial resolution finer than the land surface model resolution (∼30 arc-min) produced the best results, with spatial resolutions coarser than the model resolution yielding only a slight degradation. Observations at half the land surface model spatial resolution were found to be appropriate for our application. Moreover, it was found that satisfying the spatial resolution and accuracy requirements was much more important than repeat time.     

Modeling internal tides over Fieberling Guyot: resolution, parameterization, performance

Terrain-following ocean models are being used to simulate baroclinic tides and provide estimates of the tidal fields for circulation and mixing studies. These models have successfully reproduced elevations with most of the remaining inaccuracies attributed to topographic errors; however, the replication of barotropic and baroclinic velocity fields has not been as robust. Part of the problem is the lack of an adequate observational dataset in the simulated regions to compare the models. This problem was addressed using a dataset collected during the Flow over Abrupt Topography initiative at Fieberling Guyot. To evaluate the capability of the Regional Ocean Model System (ROMS) to simulate baroclinic tidal velocities, the combined tides for four constituents, M₂, S₂, K₁, and O₁, were modeled over Fieberling Guyot. Model inputs, numerical schemes, and parameterizations were varied to improve agreement with observations. These included hydrography, horizontal resolution, and the vertical mixing parameterization. Other factors were evaluated but are not included in this paper. With the best case, semidiurnal baroclinic tides were well replicated with RMS differences between the model estimates and the observations of 1.85 and 0.60 cm s⁻¹ for the major axes of the tidal ellipses for M₂ and S₂, respectively. However, diurnal K₁ baroclinic tides were poorly simulated with RMS differences of 4.49 cm s⁻¹. In the simulations, the K₁ baroclinic tides remained bottom-trapped unlike the observed fields, which had free waves due to the contribution of the mean velocity to the potential vorticity. The model did not adequately simulate the mean velocity, and the K₁ tides remained trapped. A resolution of 1 km most accurately reproduced the major axes and mean velocities; however, a 4-km resolution was sufficient for a qualitative estimate of where baroclinic tidal generation occurred. Nine vertical mixing parameterizations were compared. The vertical mixing parameterization was found to have minor effects on the velocity fields, with most effects occurring over the crown of guyot and in the lower water column; however, it had dramatic effects on the estimation of vertical diffusivity of temperature. Although there was no definitive best performer for the vertical mixing parameterization, several parameterizations could be eliminated based on comparison of the vertical diffusivity estimates with observations. The best performers were Mellor–Yamada and three generic length scale schemes.     

Development of a new operational system for monitoring and forecasting coastal and open-ocean states around Japan

We developed a new system to monitor and forecast coastal and open-ocean states around Japan for operational use by the Japan Meteorological Agency. The system consists of an eddy-resolving analysis model based on four-dimensional variational assimilation and a high (2-km) resolution forecast model covering Japanese coastal areas that incorporates an initialization scheme with temporal and spatial filtering. Assimilation and forecast experiments were performed for 2008 to 2017, and the results were validated against various observation datasets. The assimilation results captured well the observed variability in sea surface temperature, coastal sea level, volume transport, and sea ice. Furthermore, the volume budget for the Japan Sea was significantly improved by the use of the 2-km resolution forecast model compared with the 10-km resolution analysis model. The forecast results indicate that this system has a predictive limit longer than 1 month in many areas, including in the Kuroshio current area south of Japan and the southern Japan Sea. In the forecast results of case studies, the 2017 Kuroshio large meander was well predicted, and warm water intrusions accompanying Kuroshio path variations south of Japan were also successfully reproduced. Sea ice forecasts for the Sea of Okhotsk largely captured the evolution of sea ice in late winter, but sea ice in early winter included relatively large errors. This system has high potential to meet operational requirements for monitoring and forecasting ocean phenomena at both meso- and coastal scales.

     

Wave ensemble forecast system for tropical cyclones in the Australian region

Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4^°) and regional (resolution 1/10^°) domains with forecast ranges of +?7 and +?3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing +?10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.     

GPS大气掩星技术在全球气候变化研究中的应用

人类活动引起全球变暖,衡量全球气候变化的指标有陆地、大气和海洋温度,水汽含量等等.研究对流层底层大气温度和水汽含量变化的传统方法是用数值天气预报模型和微波声纳,尚未实现用全球均匀覆盖的数据来做精确的定量研究.和GNSS系列卫星计划比较,最近发射的COSMIC卫星气象探测数据的空间、时间以及垂直分辨率都大大提高.采用COSMIC数据可以改进和量化南极洲的大气压力模型,并综合GNSS系列卫星测量的水汽和温度剖面研究全球气候变化.用一维协方差算法估计南极洲及附近海洋的大气压、温度和湿度剖面.把COSMIC卫星密集测量期间演算得到的大气折射率和GNSS系列卫星的结果进行比较.再和独立测量数据进行比较,包括南极洲自动气象观测站资料,数值天气预报模型资料,多种测高卫星水汽资料和海洋表面温度资料以及区域GPS水汽图.上述工作将改进发展中的气象遥感技术并应用于天气预报和空间天气预报及全球气候变化研究.     

On the assessment of Argo float trajectory assimilation in the Mediterranean Forecasting System

The Mediterranean Forecasting System (MFS) has been operational for a decade, and is continuously providing forecasts and analyses for the region. These forecasts comprise local- and basin-scale information of the environmental state of the sea and can be useful for tracking oil spills and supporting search-and-rescue missions. Data assimilation is a widely used method to improve the forecast skill of operational models and, in this study, the three-dimensional variational (OceanVar) scheme has been extended to include Argo float trajectories, with the objective of constraining and ameliorating the numerical output primarily in terms of the intermediate velocity fields at 350 m depth. When adding new datasets, it is furthermore crucial to ensure that the extended OceanVar scheme does not decrease the performance of the assimilation of other observations, e.g., sea-level anomalies, temperature, and salinity. Numerical experiments were undertaken for a 3-year period (2005–2007), and it was concluded that the Argo float trajectory assimilation improves the quality of the forecasted trajectories with ~15%, thus, increasing the realism of the model. Furthermore, the MFS proved to maintain the forecast quality of the sea-surface height and mass fields after the extended assimilation scheme had been introduced. A comparison between the modeled velocity fields and independent surface drifter observations suggested that assimilating trajectories at intermediate depth could yield improved forecasts of the upper ocean currents.     

A climatological comparison of column-integrated water vapor for the third-generation reanalysis datasets

The atmospheric reanalysis datasets have been widely used to understand the variability of atmospheric water vapor on various temporal and spatial scales for climate change research. The difference among a variety of reanalysis datasets, however, causes the uncertainty of corresponding results. In this study, the climatology of atmospheric column-integrated water vapor for the period from 2000 to 2012 was compared among three latest third-generation atmospheric reanalyses including European Centre for Medium-range Weather Forecasts Interim Re-Analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR), while possible explanation on the difference between them was given. The results show that there are significant differences among three datasets in the multi-year global distribution, variation of interannual cycle, long-term trend and so on, though high similarity for principal mode describing the variability of water vapor. Over oceans, the characteristics of long-term CWV variability are similar, whereas the main discrepancy among three datasets is located around the equatorial regions of the Intertropical Convergence Zone, the South Pacific Convergence Zone and warm cloud area, which is related with the difference between reanalysis models for the scheme of convective parameterization, the treatment of warm clouds, and the assimilation of satellite-based observations. Moreover, these CWV products are fairly consistent with observations (satellite-based retrievals) for oceans. On the other hand, there are systematic underestimations about 2.5 kg/m² over lands for all three CWV datasets, compared with radiosonde observations. The difference between models to solve land-atmosphere interaction in complex environment, as well as the paucity in radiosonde observations, leads to significant water vapor gaps in the Amazon Basin of South America, central parts of Africa and some mountainous regions. These results would help better understand the climatology difference among various reanalysis datasets better, and more properly choose water vapor datasets for different research requirements.     

中国南部对流层中上层臭氧增加的气象场判识及臭氧变化的多尺度特征

副热带急流对中国南部地区对流层中上层臭氧浓度的影响程度及地理范围目前还研究较少,且缺乏综合使用常规气象资料及卫星资料来判识对流层中上层臭氧浓度增高的方法.本文利用NCEP再分析与最终分析资料、日本GMS-5地球静止卫星水汽云图资料,以2001年3月27~29日中国南部的临安、昆明、香港臭氧探测个例为基础,结合1996年3月29日香港与2001年4月13日临安对流层中上层高浓度臭氧分布个例对副热带急流对中国南部对流层中上层臭氧浓度的影响进行了详细分析,提出根据气象要素场判识春季中国南部对流层中上层臭氧浓度增高的充分条件为根据卫星水汽图像上的暗区、高空急流入口区的左侧辐合区、高空锋区、对流层中上层≥1 PVU的向下伸展的舌状高位涡区来综合判断.本文的分析结果表明,本文个例中对流层中上层高浓度臭氧来自平流层;香港对流层中上层低浓度臭氧来自热带海洋地区.不仅臭氧垂直廓线的多个极小与极大值表明臭氧垂直分布的多尺度变化特征,而且对流层中上层PV分布以及卫星水汽图像分析也表明大气中的多尺度运动对臭氧垂直分布特征有显著影响.本文的结果表明与副热带高空急流相联系的平流层空气侵入不仅发生在中国大陆的较高纬度地区,较低纬度的昆明与香港地区也有平流层空气侵入导致对流层中上层臭氧浓度升高.     

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.     

Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques

Snow accumulation and melt is highly variable in space and time in complex mountainous environments. Therefore, it is necessary to provide high‐resolution spatially and temporally distributed estimates of sub‐basin snow water equivalent (SWE) to accurately predict the timing and magnitude of snowmelt runoff. In this study, we compare two reconstruction techniques (a commonly used deterministic reconstruction vs a probabilistic data assimilation framework). The methods retrospectively estimate SWE from a time series of remotely sensed maps of fractional snow‐covered area (FSCA). In testing both methods over the Tokopah watershed in the Sierra Nevada (California), the probabilistic reconstruction approach is shown to be a more robust generalization of the deterministic reconstruction. Under idealized conditions, both probabilistic and deterministic approaches perform reasonably well and yield similar results when compared with in situ verification data, whereas the probabilistic reconstruction was found to be in slightly better agreement with snow‐pit observations. More importantly, the probabilistic approach was found to be more robust: unaccounted for biases in solar radiation impacted the probabilistic SWE estimates less than the deterministic case (4% vs 7% errors for water year (WY)1997 and 0% vs 3% errors for WY1999); the probabilistic reconstruction was found to be less sensitive to the number of available observations (6% vs 10% errors in WY1997 and 13% vs 44% errors in WY1999 from the nominal cases when four fewer FSCA images were available). Finally, results from the probabilistic reconstruction approach, which requires precipitation inputs (unlike the deterministic approach), were found to be relatively robust to bias in prior precipitation estimates, where the nominal case mean estimates were recovered even when an underestimated prior precipitation was used. The additional robustness of the probabilistic SWE reconstruction technique should prove useful in future applications over larger basins and longer periods in mountainous terrain. Copyright © 2013 John Wiley & Sons, Ltd.     

Utilization of satellite data in land surface hydrology: sensitivity and assimilation

This paper investigates the sensitivity of potential evapotranspiration to input meteorological variables, i.e. surface air temperature and surface vapor pressure. The sensitivity studies have been carried out for a wide range of land surface variables such as wind speed, leaf area index and surface temperatures. Errors in the surface air temperature and surface vapor pressure result in errors of different signs in the computed potential evapotranspiration. This result has implications for use of estimated values from satellite data or analysis of surface air temperature and surface vapor pressure in large‐scale hydrological modeling. The comparison of cumulative potential evapotranspiration estimates using ground observations and satellite observations over Manhattan, Kansas for a period of several months shows a variable difference between the two estimates. The use of satellite estimates of surface skin temperature in hydrological modeling to update the soil moisture using a physical adjustment concept is studied in detail, including the extent of changes in soil moisture resulting from the assimilation of surface skin temperature. The soil moisture of the 1 cm surface layer was adjusted by 0·9 mm over a 10‐day period as a result of a 3 K difference between the predicted and the observed surface temperature. This is a considerable amount given the fact that the top layer can hold only 5 mm of moisture. Copyright © 2001 John Wiley & Sons, Ltd.