AJirst principles-based model was developed for a transcritical C02 gas cooler, using a finite element method. The model uses published correlations for refrigerant and airside heat transfer and pressure drop. Experimental results are presented at 48 operating conditions. The model predicted the gas cooler capacity within ±2% and pressure drop on the R744 side well within the range of experimental error. The model's usefulness is demonstrated by analyzing alternative circuiting and multi-slab designs.

A variety of research has linked high temperature to outdoor thermal comfort in summer, but it remains unclear how outdoor meteorological environments influence people's thermal sensation in subtropical monsoon climate areas, especially in China. In order to explain the process, and to better understand the related influential factors, we conducted an extensive survey of thermally comfortable conditions in open outdoor spaces. The goal of this study was to gain an insight into the subjects' perspectives on weather variables and comfort levels, and determine the factors responsible for the varying human thermal comfort response in summer. These perceptions were then compared to actual ambient conditions. The database consists of surveys rated by 205 students trained from 6:00 am to 8:00 pm outdoors from 21 to 25 August 2009, at Nanjing University of Information Science & Technology (NUIST), Nanjing, China. The multiple regression approach and simple factor analysis of variance were used to investigate the relationships between thermal comfort and meteorological environment, taking into consideration individual mood, gender, level of regular exercise, and previous environmental experiences. It was found that males and females have similar perceptions of maximum temperature; in the most comfortable environment, mood appears to have a significant influence on thermal comfort, but the influence of mood diminishes as the meteorological environment becomes increasingly uncomfortable. In addition, the study confirms the strong relationship between thermal comfort and microclimatic conditions, including solar radiation, atmospheric pressure, maximum temperature, wind speed and relative humidity, ranked by importance. There are also strong effects of illness, clothing and exercise, all of which influence thermal comfort. We also find that their former place of residence influences people's thermal comfort substantially by setting expectations. Finally, some relationships between thermal perception and amount of exercise, thermal experience, mood, clothing, illness and microclimate, etc., are established. Our findings also shed light on how to resist or adapt to outdoor hyperthermic conditions during summer in subtropical monsoon climate areas.

Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

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The ensemble Kalman filter (EnKF) has been extensively applied in sequential soil moisture data assimilation to improve the land surface model performance and in turn weather forecast capability. Usually, the ensemble size of EnKF is determined with limited sensitivity experiments. Thus, the optimal ensemble size may have never been reached. In this work, based on a series of mathematical derivations, we demonstrate that the maximum efficiency of the EnKF for assimilating observations into the models could be reached when the ensemble size is set to 12. Simulation experiments are designed in this study under ensemble size cases 2, 5, 12, 30, 50, 100, and 300 to support the mathematical derivations. All the simulations are conducted from 1 June to 30 September 2012 over southeast USA (from −90°W, 30°N to −80°W, 40°N) at 25 km resolution. We found that the simulations are perfectly consistent with the mathematical derivation. This optical ensemble size may have theoretical implications on the implementation of EnKF in other sequential data assimilation problems.

A global Soil Moisture Operational Product System (SMOPS) has been developed to process satellite soil moisture observational data at the NOAA National Environmental Satellite, Data, and Information Service for improving numerical weather prediction (NWP) models at the NOAA National Weather Service (NWS). A few studies have shown the benefits of assimilating satellite soil moisture data in land surface models (LSMs), which are the components of most NWP models. In this study, synthetic experiments are conducted to determine how soil moisture data quality control may impact the benefit of their assimilation into LSMs. It is found that using green vegetation fraction to quality control the SMOPS soil moisture product may significantly increase the benefit of assimilating it into Noah LSM in terms of increasing the agreement of Noah LSM surface and root zone soil moisture simulations with the corresponding in situ measurements. The quality control procedures and parameters are suggested for the assimilation of SMOPS data into NWS NWP models.

Soil moisture (SM) data from Soil Moisture Operational Product System (SMOPS) have been available and used by users since 2013, and the latest version (3.0) has been operationally released since 2017. The version 3.0 provides a combination of currently all available daily global microwave SM retrievals including observations of ASCATA, ASCATB, SMAP, SMOS, and AMSR2 from 1 April 2015 to present. This study intercompares Noah model skills with benefits of assimilating the SMOPS blended (hereafter, SMOPS) and the five individual satellite SM retrievals. Results show that SMOPS SM product presents a significant advantage in data availability in comparison with the individual SM retrievals. Significant differences in data availability, climatology, and dynamic range of SM values between the bias‐corrected SMOPS and individual SM data lead to remarkable distinctions in Noah model SM simulations. Significant improvements of assimilating individual and blended satellite SM retrievals on model SM simulations versus the open loop in both surface and root zone soil layers are evident with reducing the Soil Climate Analysis Network measurements‐based root mean square errors and raising the correlations with respect to the enhanced vegetation index. Compared to the individual SM assimilations, model SM estimations with benefits of assimilating the SMOPS data provide the more remarkable improvements in surface soil layer.

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