Noah-MP Land Surface Model Research Articles

Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km2 pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.

Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

Quantitative assessment of the parameterization sensitivity of the Noah-MP land surface model with dynamic vegetation using ChinaFLUX data

The Noah land surface model with multiparameterization options (Noah-MP) depicts the water, heat, and carbon exchange processes occurring between the land and atmosphere. Including dynamic vegetation in the model gives tens of thousands of possible model parameterization combinations, making it difficult to select the optimal scheme for practical applications. Quantitative assessment of parameterization sensitivity can mitigate this difficulty. In this study, we combined summertime observation data from eight sites in the ChinaFLUX network with the multiple parameterization schemes of four physical subprocesses to design 72 simulation tests for each site to assess the model. Subsequently, Sobol sensitivity analysis was performed to quantitatively evaluate the sensitivity of the four physical subprocesses in simulating sensible heat flux (SHF), latent heat flux (LHF), and net ecosystem exchange (NEE). These subprocesses include the soil moisture factor which controls stomatal resistance (BTR), surface turbulence exchange coefficient (SFC), runoff and groundwater (RUN), and radiation transfer (RAD). Finally, the role of the sensitive subprocess parameterization schemes on the simulation results was explored. The results showed that a combination of parameterization schemes reasonably simulated diurnal variations in SHF and LHF, although a significant difference was shown in simulated NEE diurnal variation. The SFC and RAD subprocess parameterizations were sensitive to simulated SHF at most sites, as were the BTR and SFC subprocesses to NEE. With respect to LHF, the sensitive processes were not consistent at each site. Comparison of the parameterization schemes of the sensitive processes showed that RAD and SFC process parameterization affected heat, water, and carbon flux mainly via the canopy and ground temperature, BTR by transpiration and carbon assimilation, and RUN by soil moisture.

Assimilation of Vegetation Conditions Improves the Representation of Drought over Agricultural Areas

AbstractThis study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.

Drought Variability over the Conterminous United States for the Past Century

AbstractWe examine the drought variability over the conterminous United States (CONUS) for 1915–2018 using the Noah-MP land surface model. We examine different model options on drought reconstruction, including optional representation of groundwater and dynamic vegetation phenology. Over our 104-yr reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise (closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau

Abstract. Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.

Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression.

The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions.

Improvement and Impacts of Forest Canopy Parameters on Noah-MP Land Surface Model from UAV-Based Photogrammetry

Taking a typical forest’s underlying surface as our research area, in this study, we employed unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including the tree height and canopy radius, which were used to improve the Noah-MP land surface model, which was conducted in the Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of the CN-Din forest followed a Weibull distribution. Then, the canopy parameter distribution was obtained, and we improved the look-up table values of the Noah-MP land surface model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux, which could decrease up to −11% in the midday while increasing up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately represent the heterogeneity of the underlying surface.

High-resolution spatially explicit land surface model calibration using field-scale satellite-based daily evapotranspiration product

High-resolution simulation of water budgets across the agricultural landscape is critically important to a variety of applications, such as precision agriculture, water resources management, and environmental quality assessment. Model-data integration has been shown to be an effective approach to reduce model uncertainties and there is a growing opportunity to improve land surface modeling through spatially explicit calibration with satellite data in recent decades. Recently, a satellite-based daily 30-m resolution evapotranspiration (ET) product BESS-STAIR has been developed, achieving a high performance and well capturing the spatial and temporal dynamics of ET across the U.S. Corn Belt. To explore the potential of high-resolution spatially explicit calibration for advancing land surface modeling at fine scales, we carried out calibration experiments for the Noah-MP land surface model (LSM) over cropland using this newly developed BESS-STAIR ET. We first used Sobol sensitivity analysis to identify the most sensitive parameters for the Noah-MP’s ET simulation. The most sensitive vegetation (minimum stomatal resistance) and soil parameters (saturated hydraulic conductivity, saturated matric potential, and a soil pore size distribution parameter) were calibrated using BESS-STAIR ET to improve model simulation of surface water balance. We conducted calibration experiments at 8 eddy covariance flux tower sites that grew maize and soybean across the U.S. Corn Belt, as well as a regional calibration study on the Spoon River watershed in Champaign, Illinois. When benchmarked with flux tower measurements, the BESS-STAIR ET–calibrated model (driven by flux tower forcing) on average reduced the RMSE of hourly ET from 61 W/m2 to 47 W/m2 for maize, and from 66 W/m2 to 53 W/m2 for soybean, and matched the performance of directly calibrating using flux tower measured ET. The regional study found that calibration using BESS-STAIR ET also improved the simulation of long-term regional water budgets and achieved better performance of ET than traditionally lumped calibration using streamflow. Further analysis revealed that the high-resolution calibration can resolve the spatial variations of ET to a certain extent, and the accuracy of the calibration can be largely attributed to the low bias and excellent long-term correlation of the BESS-STAIR ET data itself. Our study thus demonstrates the effectiveness of high-resolution model calibration and provides important implications in field-scale hydrological modeling and precision agricultural applications.

Examination of seasonal water and carbon dynamics in eastern Amazonia: a comparison of Noah-MP and MODIS

The Amazon region of Brazil is a vitally important region for water and carbon cycling both for the region and the globe. This region is experiencing the impacts of global climate change as well as local land cover changes. Here, we investigated water and carbon estimates and related remotely sensed variables from both MODIS satellite and the Noah-MP land surface model for 3 years (2015–2017) in the state of Mato Grosso, Brazil. Land surface temperature agrees well between MODIS and the model, while the leaf area index (LAI) is higher in the model simulations. The monthly evapotranspiration (ET) from MODIS (MOD16A2) and gross primary productivity (GPP, MOD17A2) were lower than, but well correlated with, the model simulations. A noticeable exception was in the Broadleaf Forest class, which accounts for approximately 50% of the land cover in the state, where the modeled LAI was out of phase with the satellite observations, resulting in significantly poorer performance in the water and carbon fluxes for that land cover class. In addition, we investigated the sensitivity of the ET and GPP to precipitation forcing. The modeled ET relationships show correlations of approximately 0.6 for all classes (Broadleaf Forest being the exception, 0.24), while the MODIS shows reduced values averaging about 0.5 (Broadleaf Forest = 0.03). The slopes of the relationships illustrated the same sensitivity between MODIS and Noah-MP with the exception of Grasslands and Open Shrublands. The GPP relationships with precipitation show lower correlations across all land cover types for both MODIS and Noah-MP, with the slopes being significantly different for the Open Shrublands and Broadleaf Forest classes. In each of these classes, the Noah-MP simulations resulted in increased sensitivity to precipitation than was observed in the MODIS products. We highlight that this comparison is essential for increasing our understanding of how these different sources estimate water and carbon cycling and can be utilized for assessing the impacts of climate and land cover change in the region.

Effects of organic soil in the Noah-MP land-surface model on simulated skin and soil temperature profiles and surface energy exchanges for China

Inclusion of the thermal and hydraulic effect of soil organic matter plays an important role in land-surface models (LSMs) for simulating soil temperature and surface energy exchanges. The current officially released Noah with multiparameterization (Noah-MP) LSM, implemented in the community Weather Research and Forecasting (WRF) model, does not include a parameterization for soil organic matter. In this study, the thermal and hydraulic effect of soil organic matter was parameterized into Noah-MP LSM. Based on the profiled soil organic matter data for China and the observations of skin temperature and soil temperature profiles from more than 2000 surface meteorological stations, we investigated the effects of organic soil using the Noah-MP LSM with and without the profiled soil organic matter at the regional scales. Compared with the simulation without the inclusion of organic soil parameterization, the Noah-MP LSM simulation with the profiled soil organic matter improved the skin temperature and soil temperature profiles, especially soil temperature in deep soils under cold and arid regions. The realistic representation of snow depth and the snow insulation dependency on snow depth were confirmed to be a pre-requisite in Noah-MP in the high latitudes. By incorporating the profiled soil organic matter, the Noah-MP LSM enlarged the regional mean sensible heat flux (SH) and lower the regional mean latent heat flux (LH). In the warm and humid regions, a relatively smaller effect of organic soil on soil temperature could lead to a larger effect on SH and LH, especially LH.

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

AbstractThe utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common. This study assesses the degree to which model agreement with observations is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequate or inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth is notably different from the 5-cm (or less) sensing depth of L-band radiometers such as NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model (RTM) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Agreement between models and observations is quantified using Kolmogorov–Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Results show that agreement of SSM and Tb with observations depends primarily on systematic biases, and the sign of those biases depends on the particular subspace being analyzed (SSM or Tb). This study concludes that the role of increased soil layer discretization on simulated soil moisture and Tb is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations.

Stone Content Influence on Land Surface Model Simulation of Soil Moisture and Evapotranspiration at Reynolds Creek Watershed

AbstractConsiderable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more five-layer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

Assimilation of vegetation optical depth retrievals from passive microwave radiometry

Abstract. Vegetation optical depth (VOD) retrievals from passive microwave sensors provide analog estimates of above-ground canopy biomass. This study presents the development and analysis of assimilating VOD retrievals from X-, C-, and L-band passive microwave instruments within the Noah-MP land surface model over the Continental U.S. The results from this study demonstrate that the assimilation of VOD retrievals have a significant beneficial impact on the simulation of evapotranspiration and GPP, particularly over the agricultural areas of the U.S. The improvements in the water and carbon fluxes from the assimilation of VOD from X- and C-band sensors are found to be comparable to those obtained from the assimilation of vegetation indices from optical sensors. The study also quantifies the relative and joint impacts of assimilating surface soil moisture and VOD from the Soil Moisture Active Passive (SMAP) mission. The utility of soil moisture assimilation for improving evapotranspiration (ET) is more significant over water-limited regions, whereas VOD DA is more impactful over areas where soil moisture is not the primary controlling factor on ET. The results also indicate that the information on moisture and vegetation states from SMAP can be simultaneously exploited through the joint assimilation of surface soil moisture and VOD. Since passive microwave-based VOD retrievals are available in nearly all weather conditions, their use within data assimilation systems offers the ability to extend and improve the utility obtained from the use of optical/infrared-based vegetation retrievals.

Towards a soil moisture drought monitoring system for South Korea

The Korea Land Data Assimilation System (KLDAS) has been established for agricultural drought (i.e. soil moisture deficit) monitoring in South Korea, running the Noah-MP land surface model within the NASA Land Information System (LIS) framework with the added value of local precipitation forcing dataset and soil texture maps. KLDAS soil moisture is benchmarked against three global products: the Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and the European Space Agency Climate Change Initiative (ESA CCI) satellite product. The evaluation is performed using in situ measurements for 2013–2015 and one month standardized precipitation index (SPI-1) for 1982–2016, focusing on four major river basins in South Korea. The KLDAS outperforms all benchmark products in capturing soil moisture states and variability at a basin scale. Compared to GLDAS and FLDAS products, the EAS CCI product is not feasible for long term agricultural monitoring due to lower data quality for early periods (1979–1991) of soil moisture estimates. KLDAS shows that the most recent 2015 drought event leads to highest drought areas in the Han and Geum River basins in the past 35 years. This work supports KLDAS as an effective agricultural drought monitoring system to provide continuous regional high-resolution soil moisture estimates in South Korea.

Distinguishing between early- and late-covering crops in the land surface model Noah-MP: impact on simulated surface energy fluxes and temperature

Abstract. Land surface models are essential parts of climate and weather models. The widely used Noah-MP land surface model requires information on the leaf area index (LAI) and green vegetation fraction (GVF) as key inputs of its evapotranspiration scheme. The model aggregates all agricultural areas into a land use class termed “cropland and pasture”. In a previous study we showed that, on a regional scale, the GVF has a bimodal distribution formed by two crop groups differing in phenology and growth dynamics: early-covering crops (ECC; e.g., winter wheat, winter rapeseed, winter barley) and late-covering crops (LCC; e.g., corn, silage maize, sugar beet). That result can be generalized for central Europe. The present study quantifies the effect of splitting the land use class cropland and pasture of Noah-MP into ECC and LCC on surface energy fluxes and temperature. We further studied the influence of increasing the LCC share, which in the study area (the Kraichgau region, southwest Germany) is mainly the result of heavily subsidized biomass production, on energy partitioning at the land surface. We used the GVF dynamics derived from high-resolution (5 m × 5 m) RapidEye satellite data and measured LAI data for the simulations. Our results confirm that the GVF and LAI strongly influence the partitioning of surface energy fluxes, resulting in pronounced differences between simulations of ECC and LCC. Splitting up the generic crop into ECC and LCC had the strongest effect on land surface exchange processes in July–August. During this period, ECC are at the senescence growth stage or already harvested, while LCC have a well-developed ground-covering canopy. The generic crop resulted in humid bias, i.e., an increase in evapotranspiration by +0.5 mm d−1 (latent heat flux is 1.3 MJ m−2 d−1), decrease in sensible heat flux (H) by 1.2 MJ m−2 d−1 and decrease in surface temperature by −1 ∘C. The bias increased as the shares of ECC and LCC became similar. The observed differences will impact the simulations of processes in the planetary boundary layer. Increasing the LCC share from 28 % to 38 % in the Kraichgau region led to a decrease in latent heat flux (LE) and a heating up of the land surface in the early growing season. Over the second part of the season, LE increased and the land surface cooled down by up to 1 ∘C.

Evaluation of Noah-MP Land-Model Uncertainties over Sparsely Vegetated Sites on the Tibet Plateau

The water budget and energy exchange over the Tibetan Plateau (TP) region play an important role on the Asian monsoon. However, it is not well presented in the current land surface models (LSMs). In this study, uncertainties in the Noah with multiparameterization (Noah-MP) LSM are assessed through physics ensemble simulations in three sparsely vegetated sites located in the central TP. The impact of soil organic matter on energy flux and water cycles, along with the influence of uncertainties in precipitation are explored using observations at those sites during the third Tibetan Plateau Experiment from 1August2014 to31July2015. The greatest uncertainties are in the subprocesses of the canopy resistance, soil moisture limiting factors for evaporation, runoff (RNF) and ground water, and surface-layer parameterization. These uncertain subprocesses do not change across the different precipitation datasets. More precipitation can increase the annual total net radiation (Rn), latent heat flux (LH) and RNF, but decrease sensible heat flux (SH). Soil organic matter enlarges the annual total LH by ~26% but lessens the annual total Rn, SH, and RNF by ~7%, 7%, and 39%, respectively. Its effect on the LH and RNF at the Nagqu site, which has a sand soil texture type, is greater than that at the other two sites with sandy loam. This study highlights the importance of precipitation uncertainties and the effect of soil organic matter on the Noah-MP land-model simulations. It provides a guidance to improve the Noah-MP LSM further and hence the land-atmosphere interactions simulated by weather and climate models over the TP region.

Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.

Summer- and Wintertime Variations of the Surface and Near-Surface Urban Heat Island in a Semiarid Environment

Abstract This paper examines summer- and wintertime variations of the surface and near-surface urban heat island (UHI) for the Phoenix metropolitan area using the Moderate Resolution Imaging Spectroradiometer (MODIS), near-surface meteorological observations, and the Weather Research and Forecasting (WRF) Model during a 31-day summer- and a 31-day wintertime period. The surface UHI (defined based on the urban–rural land surface temperature difference) is found to be higher at night and during the warm season. On the other hand, the morning surface UHI is low and frequently exhibits an urban cool island that increases during the summertime period. Similarly, the near-surface UHI (defined based on the urban–rural 2-m air temperature difference) is higher at night and during summertime. On the other hand, the daytime near-surface UHI is low but rarely exhibits an urban cool island. To evaluate the WRF Model’s ability to reproduce the diurnal cycle of near-surface meteorology and surface skin temperature, two WRF Model experiments (one using the Bougeault and Lacarrere turbulent scheme and one with the Mellor–Yamada–Janjić turbulent parameterization) at high spatial resolution (1-km horizontal grid spacing) are conducted for each 31-day period. Modeled results show that the WRF Model (coupled to the Noah-MP land surface model) tends to underestimate to some extent surface skin temperature during daytime and overestimate nighttime values during the wintertime period. In the same way, the WRF Model tends to accurately reproduce the diurnal cycle of near-surface air temperature, including maximum and minimum temperatures, and wind speed during summertime, but notably overestimates nighttime near-surface air temperature during wintertime. This nighttime overestimation is especially remarkable with the Bougeault and Lacarrere turbulent scheme for both urban and rural areas.

An optimal ensemble of the Noah-MP land surface model for simulating surface heat fluxes over a typical subtropical forest in South China

Accurate estimation of land surface heat fluxes is critical to climate and air quality forecasting, which needs an appropriate parameterization in land surface models. The benchmarking method was applied to search for the optimal combination of the physical parameterization based on Noah land surface model with multi-physics options (Noah-MP). The effect of integrated physical mechanisms on the simulation of land surface energy fluxes in a typical subtropical forest (Dinghushan Forest Ecosystem Research Station) over South China is further investigated. The most sensitive process is the soil moisture threshold for evaporation while there are six processes have little or no effect on heat simulation. The optimal combination options are referred as follow: prescribed table leaf area index (LAI) and vegetation fraction, Jarvis scheme for canopy stomatal resistance, Noah soil moisture scheme, SIMGM model for runoff and groundwater, original Noah consistent scheme for surface layer drag coefficient and two-stream applied to grid-cell scheme for radiation transfer.