Improvement of the Noah land surface model for warm season processes: evaluation of water and energy flux simulation

The Noah model is a land surface model of the National Centers for Environmental Prediction. It has been widely used in regional coupled weather and climate models (i.e. Weather Research and Forecasting Model, Eta Mesoscale Model) and global coupled weather and climate models (i.e. National Centers for Environmental Prediction Global Forecast System, Climate Forecast System). Therefore, its continued improvement and development are keys to enhancing our weather and climate forecast ability and water and energy flux simulation accuracy. North American Land Data Assimilation System phase 1 (NLDAS‐1) experiments indicated that the Noah model exhibited substantial bias in latent heat flux, total runoff and land skin temperature during the warm season, and such bias can significantly affect coupled weather and climate models. This paper presents a study to improve the Noah model by adding model parameterization processes such as including seasonal factor on leaf area index and root distribution and selecting optimal model parameters. We compared simulated latent heat flux, mean annual runoff and land skin temperature from the Noah control and test versions with measured latent heat flux, land surface skin temperature, mean annual runoff and satellite‐retrieved land surface skin temperature. The results show that the test version significantly reduces biases in latent heat, total runoff and land skin temperature simulation. The test version has been used for the NLDAS phase 2 (NLDAS‐2) to produce 30‐year water flux, energy flux and state variable products to support the US drought monitor of National Integrated Drought Information System. Copyright © 2012 John Wiley & Sons, Ltd.     

Intercomparison of oceanic and atmospheric forced and coupled mesoscale simulations. Part I: Surface fluxes

A mesoscale non-hydrostatic atmospheric model has been coupled with a mesoscale oceanic model. The case study is a four-day simulation of a strong storm event observed during the SEMAPHORE experiment over a 500 × 500 km² domain. This domain encompasses a thermohaline front associated with the Azores current. In order to analyze the effect of mesoscale coupling, three simulations are compared: the first one with the atmospheric model forced by realistic sea surface temperature analyses; the second one with the ocean model forced by atmospheric fields, derived from weather forecast re-analyses; the third one with the models being coupled. For these three simulations the surface fluxes were computed with the same bulk parametrization. All three simulations succeed well in representing the main oceanic or atmospheric features observed during the storm. Comparison of surface fields with in situ observations reveals that the winds of the fine mesh atmospheric model are more realistic than those of the weather forecast re-analyses. The low-level winds simulated with the atmospheric model in the forced and coupled simulations are appreciably stronger than the re-analyzed winds. They also generate stronger fluxes. The coupled simulation has the strongest surface heat fluxes: the difference in the net heat budget with the oceanic forced simulation reaches on average 50 Wm⁻² over the simulation period. Sea surface-temperature cooling is too weak in both simulations, but is improved in the coupled run and matches better the cooling observed with drifters. The spatial distributions of sea surface-temperature cooling and surface fluxes are strongly inhomogeneous over the simulation domain. The amplitude of the flux variation is maximum in the coupled run. Moreover the weak correlation between the cooling and heat flux patterns indicates that the surface fluxes are not responsible for the whole cooling and suggests that the response of the ocean mixed layer to the atmosphere is highly non-local and enhanced in the coupled simulation.     

Calibration of an integrated land surface process and radiobrightness (LSP/R) model during summertime

In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types.     

Tests of data assimilation and data initialization for the purposes of short-range weather forecast

Summary The initialization and assimilation of input data were studied and tested by the adiabatic version of a simple numerical model for short-range weather forecast.The initialization was based on the utilization of a digital filter technique. The method succeeded in removing high-frequency oscillations from prognostic pressure fields. However, excessive smoothing deteriorated the accuracy of the prediction at the lowest levels of the atmosphere.The data assimilation was performed using the nudging method. Three versions of the nudging method in a splitting scheme were tested. The inclusion of the assimilation at the end of the integration step proved to be the best. The assimilation damped the oscillations of prognostic surface pressure fields and slightly improved the pressure prediction at the lowest levels of the atmosphere.     

Initialisation of Land Surface Variables for Numerical Weather Prediction

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.     

Investigation of the impacts of vegetation distribution and evaporative cooling on synthetic urban daytime climate using a coupled LES—LSM model

Local flow properties and regional weather or climate are strongly affected by land‐atmosphere interactions of momentum and scalars within the daytime convective boundary layer (CBL). In this study, we investigate the impact of green space scale on the daytime atmospheric boundary layer (ABL) over a synthetic urban domain using a recently developed large‐eddy simulation‐land surface model (LES–LSM) framework. With the use of realistic soundings as initial conditions, a series of numerical experiments over synthetic urban surfaces with varied scale of vegetated area is performed. Simulated micrometeorological properties, surface fluxes, basic CBL characteristics, and cloud distribution are analysed. The results show reference‐level air potential temperature and specific humidity as well as surface fluxes over green space are significantly affected by the scale of green space in the urban domain. The surface organization due to vegetated area scale also has impacts on horizontally averaged scalar and momentum profiles; however, the magnitude in this study is smaller than the results of a previous study using a set of offline surface fluxes as the lower boundary condition for LES. In addition, even though this study only performs a daytime diurnal cycle, the impact of green space scale on cloud distribution in simulations is significant. The cases with more organized green space yield lower‐elevated cumulus cloud and larger‐cloud cover fraction, which impacts the energy budget at the top of boundary layer and, in turn, could lead to additional surface cooling with respect to longer‐term weather and climate. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.     

Spatial characterization of land surface energy fluxes and uncertainty estimation at the Salar de Atacama,Northern Chile

We use Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data to estimate spatial energy flux and evaporation distributions at the Salar de Atacama, a playa in Northern Chile. Our approach incorporates ASTER surface kinetic temperature, emissivity, and reflectance data, ground-based meteorological measurements, and empirical parameters. Energy flux distributions are estimated using either spatially constant or spatially distributed values of model parameters, with spatially distributed parameters assigned separately to each land cover category in an image classification. We test the sensitivity of energy budget calculations to state variable and parameter values by conducting Monte Carlo simulations for regions with ground energy budget measurements. Results show that assigning spatially distributed model parameters via land cover classifications yields significant improvements to ground and sensible heat flux predictions. Latent heat fluxes cannot, however, be predicted with sufficient accuracy to allow estimation of area-integrated evaporative moisture loss at this low-evaporation playa.     

Modeling studies of the upper ocean response to a tropical cyclone

A coupled ocean and boundary layer flux numerical modeling system is used to study the upper ocean response to surface heat and momentum fluxes associated with a major hurricane, namely, Hurricane Dennis (July 2005) in the Gulf of Mexico. A suite of experiments is run using this modeling system, constructed by coupling a Navy Coastal Ocean Model simulation of the Gulf of Mexico to an atmospheric flux model. The modeling system is forced by wind fields produced from satellite scatterometer and atmospheric model wind data, and by numerical weather prediction air temperature data. The experiments are initialized from a data assimilative hindcast model run and then forced by surface fluxes with no assimilation for the time during which Hurricane Dennis impacted the region. Four experiments are run to aid in the analysis: one is forced by heat and momentum fluxes, one by only momentum fluxes, one by only heat fluxes, and one with no surface forcing. An equation describing the change in the upper ocean hurricane heat potential due to the storm is developed. Analysis of the model results show that surface heat fluxes are primarily responsible for widespread reduction (0.5°–1.5°C) of sea surface temperature over the inner West Florida Shelf 100–300 km away from the storm center. Momentum fluxes are responsible for stronger surface cooling (2°C) near the center of the storm. The upper ocean heat loss near the storm center of more than 200 MJ/m² is primarily due to the vertical flux of thermal energy between the surface layer and deep ocean. Heat loss to the atmosphere during the storm’s passage is approximately 100–150 MJ/m². The upper ocean cooling is enhanced where the preexisting mixed layer is shallow, e.g., within a cyclonic circulation feature, although the heat flux to the atmosphere in these locations is markedly reduced.     

Vegetation physiological parameter setting in the Simple Biosphere model 2(SiB2) for alpine meadows in the upper reaches of Heihe river

Land surface process modeling of high and cold area with vegetation cover has not yielded satisfactory results in previous applications. In this study, land surface energy budget is simulated using a land surface model for the A’rou meadow in the upper-reach area of the Heihe River Basin in the eastern Tibetan Plateau. The model performance is evaluated using the in-situ observations and remotely sensed data. Sensible and soil heat fluxes are overestimated while latent heat flux is underestimated when the default parameter setting is used. By analyzing physical and physiological processes and the sensitivities of key parameters, the inappropriate default setting of optimum growth and inhibition temperatures is identified as an important reason for the bias. The average daytime temperature during the period of fastest vegetation growth(June and July) is adopted as the optimum growth temperature, and the inhibition temperatures were adjusted using the same increment as the optimum temperature based on the temperature acclimation. These adjustments significantly reduced the biases in sensible, latent, and soil heat fluxes.     

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.     

A review of the estimation of downward surface shortwave radiation based on satellite data: Methods,progress and problems

The estimation of downward surface shortwave radiation(DSSR) is important for the Earth's energy budget and climate change studies. This review was organised from the perspectives of satellite sensors, algorithms and future trends,retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years. The shortwave radiation reaching the Earth's surface is affected by both atmospheric and land surface parameters. In recent years,studies have given detailed considerations to the factors which affect DSSR. It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces(such as snow-covered areas) on DSSR estimation. This review classified DSSR retrieval methods into four categories:empirical, parameterisation, look-up table and machine-learning methods, and evaluated their advantages, disadvantages and accuracy. Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud, haze, water vapor and other land surface parameters such as albedo of complex terrain and bright surface, organically combine machine learning and other methods, use the new-generation geostationary satellite and polar orbit satellite data to produce highresolution DSSR products, and promote the application of radiation products in hydrological and climate models.     

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.     

Data assimilation methods in the Earth sciences

Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology.     

Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

A land surface hydrology parameterization for use in atmospheric GCMs is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large scale parameterizations: water balance calculations are performed for a number of intervals of the topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well at the dynamics of land surface-atmosphere interactions.     

近地层能量闭合度对陆面过程模式影响

大量近地层观测试验表明,利用涡动相关法观测的湍流通量小于近地层可利用能量,即近地层能量是不闭合的,这种不闭合度一般为20%甚至更高.而陆面过程模式是基于地气间能量平衡建立,并且模式中的湍流边界层参数化方案通常根据实际观测的湍流通量来确定,因此能量不闭合必将对陆面过程模式造成一定的影响.本文利用2007年春季SACOL站的近地层观测资料,依据能量守恒将能量不闭合中的残余能量通过波文比分配到观测的湍流通量中,即修正涡动相关法观测的湍流通量使得近地层能量达到平衡;之后分别利用观测和修正的湍流通量,建立了能量不闭合和闭合情形下的湍流参数化方案,借助陆面过程模式SHAW,通过数值模拟和对比分析方法考察近地层能量闭合度对陆面过程模式的影响.研究结果表明近地层能量闭合对陆面过程模式有显著的影响:在陆面过程数值模拟中,当应用近地层能量不闭合的湍流通量形成的湍流参数化方案时,陆面过程模式会明显高估地表长波辐射及土壤温度;但当应用修正湍流通量使得近地层能量达到闭合形成的湍流参数化方案后,在不改变任何地表土壤物理生化属性的情况下,陆面过程模式能较好地模拟地表长波辐射和土壤温度.     

Numerical simulation and data assimilation of the water-energy cycle over semiarid northeastern China

The default fractional vegetation cover and terrain height were replaced by the estimated fractional vegetation cover, which was calculated by the Normalized Difference Vegetation Index(NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer(EOS-MODIS) and the Digital Elevation Model of the Shuttle Radar Topography Mission(SRTM) system. The near-surface meteorological elements over northeastern China were assimilated into the three-dimensional variational data assimilation system(3DVar) module in the Weather Research and Forecasting(WRF) model. The structure and daily variations of air temperature, humidity, wind and energy fields over northeastern China were simulated using the WRF model. Four groups of numerical experiments were performed, and the simulation results were analyzed of latent heat flux, sensible heat flux, and their relationships with changes in the surface energy flux due to soil moisture and precipitation over different surfaces. The simulations were compared with observations of the stations Tongyu, Naiman, Jinzhou, and Miyun from June to August, 2009. The results showed that the WRF model achieves high-quality simulations of the diurnal characteristics of the surface layer temperature, wind direction, net radiation, sensible heat flux, and latent heat flux over semiarid northeastern China in the summer. The simulated near-surface temperature, relative humidity, and wind speed were improved in the data assimilation case(Case 2) compared with control case(Case 1). The simulated sensible heat fluxes and surface heat fluxes were improved by the land surface parameterization case(Case 3) and the combined case(Case 4). The simulated temporal variations in soil moisture over the northeastern arid areas agree well with observations in Case 4, but the simulated precipitation should be improved in the WRF model. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations. The assimilation datasets generated by this work can be applied to research on climate change and environmental monitoring of arid lands, as well as research on the formation and stability of climate over semiarid areas.     

沙漠绿洲地区夏季地表能量收支的数值模拟

本文在MSPAS(Modified Soil-Plant-Atmosphere Scheme)的基础上,引入了一个有效的晴天大气辐射传输方案,建立了一个能在物理上真实地模拟陆气相互作用及其反馈机制的二维模式MLAIM（Modified Land Atmosphere Interaction Model）.本文利用HEIFE实验的观测资料对MLAIM的模拟结果进行了检验,对其中不合理的部分进行了分析,指出了在干旱半干旱区陆面过程参数修正的必要性,对干旱半干旱区土壤水分传输以及大气近地面层湍流输送的参数化方案进行了改进.改进后的模式能够较好地模拟夏季连续晴天条件下沙漠的地表能量收支,因此,本文利用MLAIM研究了绿洲对其周围沙漠地表能量收支的影响,并对地表能量收支各分量之间的相互作用进行了分析.结果表明,绿洲向其下风向沙漠的水汽输送是导致其上下风向沙漠间地表能量收支差异的最重要的因子.     

Diurnal cycle of surface energy fluxes in high mountain terrain: High-resolution fully coupled atmosphere-hydrology modelling and impact of lateral flow

Water and energy fluxes are inextricably interlinked within the interface of the land surface and the atmosphere. In the regional earth system models, the lower boundary parameterization of land surface neglects lateral hydrological processes, which may inadequately depict the surface water and energy fluxes variations, thus affecting the simulated atmospheric system through land-atmosphere feedbacks. Therefore, the main objective of this study is to evaluate the hydrologically enhanced regional climate modelling in order to represent the diurnal cycle of surface energy fluxes in high spatial and temporal resolution. In this study, the Weather Research and Forecasting model (WRF) and coupled WRF Hydrological modelling system (WRF-Hydro) are applied in a high alpine catchment in Northeastern Tibetan Plateau, the headwater area of the Heihe River. By evaluating and intercomparing model results by both models, the role of lateral flow processes on the surface energy fluxes dynamics is investigated. The model evaluations suggest that both WRF and coupled WRF-Hydro reasonably represent the diurnal variations of the near-surface meteorological fields, surface energy fluxes and hourly partitioning of available energy. By incorporating additional lateral flow processes, the coupled WRF-Hydro simulates higher surface soil moisture over the mountainous area, resulting in increased latent heat flux and decreased sensible heat flux of around 20–50 W/m² in their diurnal peak values during summertime, although the net radiation and ground heat fluxes remain almost unchanged. The simulation results show that the diurnal cycle of surface energy fluxes follows the local terrain and vegetation features. This highlights the importance of consideration of lateral flow processes over areas with heterogeneous terrain and land surfaces.