High-Resolution Land Data Assimilation System Research Articles

Improving ecological indicators of arid zone deserts through simulation

Ecological indicators, such as soil moisture content and gross primary production (GPP), play important roles in the management of ecosystems and water resources and within climate change research. However, studies on monitoring and simulation of ecological indicators of arid zones remain limited. This study simulated soil moisture (SM), latent heat (LH), and gross primary productivity (GPP) for the arid Yarkant River Basin in China using the High-Resolution Land Data Assimilation System and Deep learning. Simulation of hydrology over a large spatial scale is difficult due to a lack of observed data and the difficulty in parameterizing models to represent complicated ecological mechanisms. A comparison of Noah Multi-Parameterization (Noah-MP) simulations to multiple datasets at an annual scale obtained correlation coefficients exceeding 0.8. The model was able to replicate the broad temporal dynamics of GPP and LH over the Yarkant River Basin. Point- and regional-scale assessments across the Yarkant River Basin obtained the optimal model performance. The temporal trends in simulated SM anomalies were consistent with observations across the different sub-basins. The simulations of the Noah-MP model driven by Global Land Data Assimilation System (GLDAS) and ERA5 data provided accurate representations of most hydrological variables, except for in the upper reaches of Yarkant River Basin, likely due to the extensive freeze-thaw activity in this transitional region. The results of this study can fill hydrological data gaps in arid zones areas.

Comparative Analysis of Three Near-Surface Air Temperature Reanalysis Datasets in Inner Mongolia Region

Near-surface air temperature is important for climate change, agriculture, animal husbandry, and ecosystems undergoing climate warming in Inner Mongolia. Land surface reanalysis products feature finer spatial and temporal resolutions, that can provide important data support for the determination of crop growth limits, grassland biomass growth, and desertification research in Inner Mongolia. In this study, 119 in situ observed sites were collected to compare and evaluate the performance of near-surface air temperature in three reanalysis products from 2018 to 2020 in Inner Mongolia. The three reanalysis products included three widely used products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5-Land), and U.S. Global Land Data Assimilation System (GLDAS), as well as the latest reanalysis product from the High-Resolution Land Data Assimilation System reanalysis product by the China Meteorological Administration (HRCLDAS). Results are as follows: (1) The three reanalysis temperature products all reasonably reflect the characteristics of spatial and temporal changes in surface temperature in Inner Mongolia. Compared with ERA5L and GLDAS, HRCLDAS is more consistent with the observed results. (2) For the evaluation period, HRCLDAS has a certain underestimation of temperature, while ERA5-Land and GLDAS have a significant overestimation of temperature. (3) During high-temperature processes, HRCLDAS is more accurate in simulating higher temperatures than ERA5-LNAD and can demonstrate the changes in high-temperature drop zones. The major conclusion of this study is that the HRCLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate, ecosystem, and sustainable development.

An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

Sensitivity of Land Surface Processes and Its Variation during Contrasting Seasons over India

The study investigates the influence of near-surface atmospheric parameters on land surface processes at the land–atmosphere interface through the offline simulation of the 2D Noah Land Surface Model-based High-Resolution Land Data Assimilation System (HRLDAS). The HRLDAS is used to conduct sensitive experiments by introducing perturbation in the atmospheric parameters, and the experiments were conducted for the period 2011–2013 in India. In each sensitive experiment, a single parameter is perturbed at a time, keeping the rest of the forcing parameters unchanged, and the procedure is followed for all the forcing parameters. The results revealed that the downward longwave radiation and T2 are highly sensitive to land surface processes, while wind speed is the least sensitive. The land surface process sensitivity varies with soil moisture content. The annual mean soil moisture at the surface layer is increased (decreased) by 8% when long wave radiation is decreased (increased) by 20%. Similarly, the annual mean soil temperature increased (decreased) by 2.2 °C when T2 increased (decreased) by 1%. The latent heat flux is highly sensitive to longwave radiation over the wetter soil, while its sensitivity to rainfall is higher over the drier soil. This is attributed to evapotranspiration’s sensitivity to the preferred soil moisture state. Further, the land surface sensitivity varies with contrasting seasons. The sensitivity of soil moisture and latent heat flux is high in OND and JJA seasons, respectively, and are least sensitive in the MAM season. In contrast, the sensible heat flux is highly sensitive to solar radiation in the MAM season and comparatively less sensitive in the JJA season. The study suggests that the antecedent soil moisture state plays a critical role in modulating land surface process sensitivity, and, therefore, a realistic soil moisture state is important for land surface feedback processes.

Improving simulation of the fog life cycle with high-resolution land data assimilation: A case study from WiFEX

The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24–25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017–18 winter months (December–January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13°C, 0.10% and 0.75 m s−1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.

WaterSmart-GIS: A Web Application of a Data Assimilation Model to Support Irrigation Research and Decision Making

Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters.

Estimating Heat-Related Exposures and Urban Heat Island Impacts: A Case Study for the 2012 Chicago Heatwave.

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts.

Simulating the Response of the Surface Urban Heat Environment to Land Use and Land Cover Changes: A Case Study of Wuhan, China

With the rapid process of urbanization, the urban heat island (UHI), the phenomenon where urban regions become hotter than their surroundings, is increasingly aggravated. The UHI is affected by multiple factors overall. However, it is difficult to dissociate the effect of one aspect by widely used approaches such as the remote-sensing-based method. To qualify the response of surface UHI to the land use and land cover (LULC) changes, this study took the numerical land model named u-HRLDAS (urbanized high-resolution land data assimilation system) as the modeling tool to investigate the effect of LULC changes on the UHI from 1980 to 2013 in Wuhan city, China. Firstly, the simulation accuracy of the model was improved, and the summer urban heat environment was simulated for the summer of 2013. Secondly, taking the simulation in 2013 as the control case (CNTL), the LULC in 1980, 1990, and 2000 were replaced by the LULC while the other conditions kept the same as the CNTL to explore the effect of LULC on UHI. The results indicate that the proper configuration of the modeling setup and accurate surface input data are considered important for the simulated results of the u-HRLDAS. The response intensity of UHI to LULC changes after 2000 was stronger than that of before 2000. From the spatial perspective, the part that had the strongest response intensity of land surface temperature to LULC changes was the region between the third ring road and the inner ring road of Wuhan. This study can provide a reference for cognizing the urban heat environment and guide policy making for urban development.

Sensitivity study of PBL schemes and soil initialization using the WRF-BEP-BEM model over a Mediterranean coastal city

Due to increased urbanization and global warming, cities are experiencing more heat wave (HW) events that cause extreme heat stress. To mitigate such effects, a better understanding of the impact of urban morphology on the boundary layer development is needed. This study investigates the sensitivity of mesoscale simulations using the WRF model coupled with the building effect parameterization and the building energy model (BEP-BEM) at a 1-km resolution to 1) soil moisture initializations; 2) the inclusion of site-specific urban morphology parameters; and 3) the planetary boundary layer (PBL) scheme. A HW episode that occurred in the metropolitan area of Barcelona serves as the case study. We find that the use of a high-resolution land data assimilation system (HRLDAS) to initialize soil properties results in larger temperature diurnal range, but it did not improve the performance of simulated temperatures compared to using low-resolution ERA5 data. The inclusion of site-specific urban parameters improved the representation of urban fractions, reducing the night-time overprediction of 2-m temperatures compared to using default urban parameters. Overall, the Bougeault-Lacarrere (BouLac) scheme represents the PBL-height noontime observations better than the Mellor-Yamada-Janjic (MYJ) scheme. This was related to a better representation of daytime near-surface temperatures by the BouLac scheme compared to the MYJ scheme

Evaluation of Regional Land Surface Conditions Developed Using The High-Resolution Land Data Assimilation System (HRLDAS) with Satellite and Global Analyses Over India

Soil moisture and temperature (SM and ST) have been identified for modeling of extreme weather and hydrological processes. The coarser resolution global analyses are limited in capturing realistic heterogeneity. This study focuses on evaluating regional land surface conditions developed from a high-resolution (4 km grid spacing) land data assimilation system (HRLDAS) over India from 2000 to 2013 against in situ and global analyses. Global analyses such as the European Space Agency Climate Change Initiative (ESACCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Forecasting System (CFS), and Global Land Data Assimilation System (GLDAS) have been considered to assess the credibility of the regional analysis. The regional SM from the HRLDAS is superior to global and satellite products, particularly in the orography (altitude > 300 m) regions followed by the plane regions (altitude ≤ 300 m). The probability distribution function (PDF) indicates that the regional SM and satellite analysis exhibited less error (~ 0.02 m3 m−3 at ~ 28%) in the plane regions. The regional SM analysis in the orography regions is reliable (0.015 m3 m−3 at 28% frequency) with a high equitable threat score (~ 0.6) compared to other analyses. The HRLDAS is consistently superior for soil temperature (ST) to other global analyses. The mean diurnal variation of HRLDAS-ST is close to in situ observation. The HRLDAS performs better for spatial representation of SM and ST for different months and monsoon seasons. The improved representation of land conditions from the HRLDAS could provide a realistic distribution of latent and sensible heat fluxes when compared with other global products. This study demonstrates the value of high-resolution regional analyses and recommends usefulness in hydrological applications.

Evaluation of a Convection-Permitting Modeling of Precipitation over the Tibetan Plateau and Its Influences on the Simulation of Snow-Cover Fraction

Abstract Precipitation is a critical input to land surface and hydrology modeling and prediction. Dynamical downscale modeling has added value to representing precipitation, when compared with the performance of coarse-resolution reanalysis and global climate models, over the Tibetan Plateau (TP). Convection-permitting modeling (CPM) may even outperform dynamical downscale models (DDMs). In this study, 4-km CPM results were compared to 28-km DDM results for a snow season (1 October 2013–31 May 2014) over the TP. The CPM- and DDM-simulated precipitation, as well as three merged gridded precipitation datasets, were evaluated against in situ observations below 4800 m. The five precipitation datasets (CPM, DDM, CMFD, COPRPH, and TRMM) showed large differences over the TP with underestimation of TRMM and overestimation of CPM and DDM compared to observations. The most significant difference occurred in the Brahmaputra Grand Canyon. Given the substantial uncertainty in observed precipitation at high mountains, snow cover simulated by a high-resolution land data assimilation system was used to indirectly evaluate the above precipitation data using MODIS observations. Simulated snow-cover fraction was greatly underestimated using all the merged precipitation datasets. However, simulations using the DDM- and CPM-generated precipitation as input outperformed those using other gridded precipitation data, showing lower biases, higher pattern correlations, and closer probability distribution functions than runs driven by the merged precipitation. The findings of this study generally support the assumption that high-resolution CPM-produced precipitation has an added value for use in land surface and hydrology simulations in high-mountain regions without reliable in situ precipitation observations.

Impact of Soil Moisture on Winter 2-m Temperature Forecasts in Northern China

AbstractIn this study, the causes of the underestimated diurnal 2-m temperature range and the overestimated 2-m specific humidity in the winter of northern China in the Rapid-Refresh Multiscale Analysis and Prediction System–Short Term (RMAPS-ST) are investigated. Three simulations based on RMAPS-ST are conducted from 1 November 2016 to 28 February 2017. Further analyses show that the partitioning of surface upward sensible heat fluxes and downward ground heat fluxes might be the main contributing factor to the 2-m temperature forecast bias. In this study, two simulations are conducted to examine the effect of soil moisture initialization and soil hydraulic property on the 2-m temperature and 2-m specific humidity forecasts. First, the High-Resolution Land Data Assimilation System (HRLDAS) is used to provide an alternative soil moisture initialization. The results show that the drier soil moisture could lead to noticeable change in energy partitioning at the land surface, which in turn results in improved prediction of the diurnal 2-m temperature range, although it also enlarges the 2-m specific humidity bias in some parts of the domain. Second, a soil texture dataset developed by Beijing Normal University and the revised hydraulic parameters are applied to provide a more detailed description of soil properties, which could further improve the 2-m specific humidity bias. In summary, the combination of using optimized soil moisture initialization, an updated soil map, and revised soil hydraulic parameters can help improve the 2-m temperature and 2-m specific humidity prediction in RMAPS-ST.

Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset (SMBFD) in China

A real-time, long-term surface meteorological blended forcing dataset (SMBFD) has been developed based on station observations, satellite retrievals, and reanalysis products in China. The observations are collected at national and regional automatic weather stations, satellite data are obtained from the Fengyun (FY) series satellites retrievals, and the reanalysis products are obtained from the ECMWF. The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission (SRTM) for temperature and humidity elevation adjustment. The dataset includes 2-m air temperature and humidity, 10-m zonal and meridional winds, downward shortwave radiation, surface pressure, and precipitation. The spatial resolution is 1 km, and the temporal resolution is 1 h. During the data processing procedure, various data fusion techniques including the space–time multiscale variational analy-sis, the discrete ordinates radiative transfer (DISORT) model, the hybrid radiation estimation model, and a terrain correction algorithm are employed. Dependent and independent evaluations of the dataset are performed against observations. The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world. The results are as follows. (1) All variables show reasonable geographic distribution features and realistic spatial and temporal variations. (2) Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations, while the dependent evaluation yields better results than the independent evaluation. (3) Compared with similar datasets produced in other meteorological operational centers, the real-time and retrospective surface meteorological fusion data obviously have higher quality. The dataset introduced in the present study is in general stable and accurate, and can be applied in various practice such as meteorology, agriculture, ecology, environmental protection, etc. Meanwhile, this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration. The dataset with the network Common Data Form (NETCDF) can be decoded by various programming languages, and it is freely available to non-commercial users.

Efficacy of Possible Strategies to Mitigate the Urban Heat Island Based on Urbanized High-Resolution Land Data Assimilation System (u-HRLDAS)

Efficacy of Possible Strategies to Mitigate the Urban Heat Island Based on Urbanized High-Resolution Land Data Assimilation System (u-HRLDAS)

Performance Evaluation of High-Resolution Land Data Assimilation System (HRLDAS) Over Indian Region

The present study evaluates the skill of a High-Resolution Land Data Assimilation System (HRLDAS) in simulating soil moisture (SM), soil temperature (ST) and sensible heat flux (SHF) for the Indian region (5°–39°N; 60°–100°E). The HRLDAS framework uses uncoupled Noah Land Surface Model (LSM) that integrates near-surface atmospheric parameters and land surface parameters from observations and analysis for the period January 2001–October 2013 at 20 km spatial resolution. The HRLDAS takes about 1 year to reach its quasi-equilibrium state for clay soil. The HRLDAS simulated ST and SM reasonably agree with the in situ observations. The simulated ST shows a negative bias in the monsoon season over the Gujarat, Mandla, and Kharagpur. The SM is under-estimated and the under-estimation increases with soil depth at Kharagpur, India. The negative bias in TRMM precipitation forcing causes under-estimation of SM. The simulated SM shown higher saturation point than observations. The daytime SHF has positive bias during the pre-monsoon, monsoon seasons and agrees well with observations in the post-monsoon season at Ranchi, India. The Noah 1D sensitivity experiments revealed that there is a need to revisit soil field capacity and porosity parameter for improving the skill of the HRLDAS.

Sensitivity of Predictions of the Urban Surface Energy Balance and Heat Island to Variations of Urban Canopy Parameters in Simulations with the WRF Model

AbstractAs NWP and climate models continue to evolve toward finer grid spacing, efforts have been undertaken to better represent urban effects. For this study, the single-layer urban canopy model (SLUCM) of the High-Resolution Land Data Assimilation System (HRLDAS) and WRF Model was used to investigate the sensitivity of near-surface air temperatures and energy fluxes to SLUCM parameters in uncoupled (land) and coupled (land–atmosphere) predictions. Output from HRLDAS and WRF was compared with observations from the Oklahoma Mesonet and Joint Urban 2003 experiment. Variations in roof albedo (0.04–0.4) produced 40–135 W m−2 changes in net radiation and sensible heat fluxes. Sensible and ground heat fluxes varied by 40–100 W m−2 with changes in roof thermal conductivity (0.05–1.4). The urban fraction was found to be the only SLUCM parameter to significantly impact latent heat fluxes. Near-surface air temperatures, particularly during the daytime, did not show significant variations with SLUCM parameters (remaining within the 0.5-K range). Differences in urban air temperatures due to the change in boundary layer scheme were greater than the temperature changes due to SLUCM parameter variations. The sensitivity of near-surface air temperatures to SLUCM parameters depended on the method used to calculate the skin temperature of the impervious surface. For all simulations, predicted 2-m urban air temperatures were consistently higher than observations, with deviations approaching 8 K during the day and below 3 K at night. These large errors affected the model’s skill in reproducing the diurnal cycle of UHI intensity.

Atmospheric water budget over the South Asian summer monsoon region

High resolution hybrid atmospheric water budget over the South Asian monsoon region is examined. The regional characteristics, variability, regional controlling factors and the interrelations of the atmospheric water budget components are investigated. The surface evapotranspiration was created using the High Resolution Land Data Assimilation System (HRLDAS) with the satellite-observed rainfall and vegetation fraction. HRLDAS evapotranspiration shows significant similarity with in situ observations and MODIS satellite-observed evapotranspiration. Result highlights the fundamental importance of evapotranspiration over northwest and southeast India on atmospheric water balance. The investigation shows that the surface net radiation controls the annual evapotranspiration over those regions, where the surface evapotranspiration is lower than 550 mm. The rainfall and evapotranspiration show a linear relation over the low-rainfall regions (<500 mm/year). Similar result is observed in in NASA GLDAS data (1980–2014). The atmospheric water budget shows annual, seasonal, and intra-seasonal variations. Evapotranspiration does not show a high intra-seasonal variability as compared to other water budget components. The coupling among the water budget anomalies is investigated. The results show that regional inter-annual evapotranspiration anomalies are not exactly in phase with rainfall anomalies; it is strongly influenced by the surface conditions and other atmospheric forcing (like surface net radiation). The lead and lag correlation of water budget components show that the water budget anomalies are interrelated in the monsoon season even up to 4 months lead. These results show the important regional interrelation of water budget anomalies on south Asian monsoon.

The Pan-Canadian High Resolution (2.5 km) Deterministic Prediction System

Abstract Since November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.

Evaluating the impact of urban morphology configurations on the accuracy of urban canopy model temperature simulations with MODIS

AbstractSimulations of the urban environment contribute to assessments of current and future urban vulnerabilities to extreme heat events. The accuracy of simulations of the urban canopy can be degraded by inaccurate or oversimplified representations of the urban‐built environment within models. Using a 10 year (2003–2012) series of offline 1 km simulations over Greater Houston with the High‐Resolution Land Data Assimilation System (HRLDAS), this study explores the model accuracy gained by progressively increasing the complexity of the urban morphology representation in an urban canopy model. The fidelity of the simulations is primarily assessed by a spatiotemporally consistent comparison of a newly developed HRLDAS radiative temperature variable with remotely sensed estimates of land surface temperature from the Moderate Resolution Imaging Spectroradiometer. The most accurate urban simulations of radiative temperature are yielded from experiments that (1) explicitly specify the urban fraction in each pixel and (2) include irrigation. The former modification yields a gain in accuracy that is larger than for other changes, such as increasing the number of urban land use types. The latter modification (irrigation) substantially reduces simulated temperature biases and increases model precision compared to model configurations that lack irrigation, presumably because watering of lawns, parks, etc. is a common activity that should be represented in urban canopy models (although it is generally not). Ongoing and future efforts to improve urban canopy model simulations may achieve important gains through better representations of urban morphology, as well as processes that affect near‐surface energy partitioning within cities, such as irrigation.

How can we use MODIS land surface temperature to validate long-term urban model simulations?

High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.