Radiative Transfer Model Simulations for Ground-Based Microwave Radiometers in North China

Ground-based microwave radiometer profilers (MWRPs) are widely used to provide high-temporal resolution atmospheric temperature and humidity profiles. The quality of the observed brightness temperature (TB) from MWRPs is key for retrieving accurate atmospheric profiles. In this study, TB simulations derived from a radiative transfer model (RTM) were used to assess the quality of TB observations. Two types of atmospheric profile data (conventional radiosonde and ERA5 reanalysis) were combined with the RTM to obtain TB simulations, then compared with corresponding observations from three MWRPs located in different places in North China to investigate the influence of input atmospheric profiles on TB simulations and evaluate the quality of TB observations from the three MWRPs. The comparisons of the matching samples under clear-sky conditions showed that TB simulations derived from both radiosonde and ERA5 profiles were very close to the TB observations from most of the MWRP channels; however, the correlation was lower and the bias was obvious at 51.26 GHz and 52.28 GHz, which indicates that the oxygen absorption component in the RTM needs to be improved for lower-frequency temperature channels. The difference in location of the radiosonde and MWRP sites affected the TB simulations for the water vapor channels, but had little impact on temperature channels that are insensitive to humidity. Comparisons of both simulations (ERA5 and Radiosonde) and the corresponding TB observations from the three sites indicated that the water vapor channels observation quality for the MWRP located in southern Beijing needs improvement. For the two types of profile data, ERA5 profiles have a more positive effect on TB simulations in the water vapor channels, such as enhanced consistence, reduced bias and standard deviation between simulations and observations for those MWRPs located away from the radiosonde station. Therefore, hourly ERA5 data are an optimal option in terms of compensating for limited radiosonde measurements and enhancing the monitoring quality of MWRP observations within 24 h.

Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station

From August to October 2020, a serious wildfire occurred in California, USA, which produced a large number of particulate matter and harmful gases, resulting in huge economic losses and environmental pollution. Particulate matter delays the GNSS signal, which affects the like precipitable water vapor (LPWV) derived by the GNSS non-hydrostatic delay. Most of the information of GNSS-derived LPWV is caused by water vapor, and a small part of the information is caused by particulate matter. A new method based on the difference (ΔPWV) between the PWV of virtual radiosonde stations network and GNSS-derived LPWV is proposed to detect the changes of particulate matter in the atmosphere during the 2020 California wildfires. There are few radiosonde stations in the experimental area and they are far away from the GNSS station. In order to solve this problem, we propose to use the multilayer perceptron (MLP) neural network method to establish the virtual radiosonde network in the experimental area. The PWV derived by the fifth-generation European center for medium-range weather forecasts reanalysis model (PWVERA5) is used as the input data of machine learning. The PWV derived by radiosonde data (PWVRAD) is used as the training target data of machine learning. The ΔPWV is obtained based on PWV derived by the virtual radiosonde station network and GNSS in the experimental area. In order to further reduce the influence of noise and other factors on ΔPWV, this paper attempts to decompose ΔPWV time series by using the singular spectrum analysis method, and obtain its principal components, subsequently, analyzing the relationship between the principal components of ΔPWV with particulate matter. The results indicate that the accuracy of PWV predicted by the virtual radiosonde network is significantly better than the fifth-generation European center for the medium-range weather forecast reanalysis model, and the change trend of ΔPWV is basically consistent with the change law of particulate matter in which the value of ΔPWV in the case of fire is significantly higher than that before and after the fire. The mean of correlation coefficients between ΔPWV and PM10 at each GNSS station before, during and after wildfires are 0.068, 0.397 and 0.065, respectively, which show the evident enhancement of the correlation between ΔPWV and particulate matter during wildfires. It is concluded that because of the high sensitiveness of ΔPWV to the change of particulate matter, the GNSS technique can be used as an effective new approach to detect the change of particulate matter and, then, to detect wildfires effectively.

Land Surface Temperature Retrieval Using High-Resolution Vertical Profiles Simulated by WRF Model

This work gives a first insight into the potential of the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing. WRF numerical simulations were conducted to downscale NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the utility of these profiles for the atmospheric correction of TIR data and LST estimation, using the moderate resolution atmospheric transmission (MODTRAN) model and the Landsat 8 TIRS10 band. The accuracy evaluation was performed using 27 clear-sky cases over a radiosonde station in Southern Brazil. We included in the comparative analysis NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles obtained directly from the NCEP CFSv2 reanalysis. The atmospheric parameters from ACPC, followed by those from CFSv2, were in better agreement with parameters calculated using in situ radiosondes. When applied into the radiative transfer equation (RTE) to retrieve LST, the best results (RMSE) were, in descending order: CFSv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). Our findings suggest that there is no special need to increase the horizontal resolution of reanalysis profiles aiming at RTE-based LST retrieval. However, the WRF results were still satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and recommend the NCEP CFSv2 profiles for TIR atmospheric correction and LST single-channel retrieval.

Inter-comparison of ABL height estimates from different profiling sensors and models in the framework of HyMeX-SOP1

This paper reports results from an inter-comparison effort involving different sensors/techniques used to measure the Atmospheric Boundary Layer (ABL) height. The effort took place in the framework of the first Special Observing Period of the Hydrological cycle of the Mediterranean Experiment (HyMeX-SOP1). Elastic backscatter and rotational Raman signals collected by the Raman lidar system BASIL were used to determine the ABL height and characterize its internal structure. These techniques were compared with co-located measurements from a wind profiler and radiosondes and with ECMWF-ERA5 data. In the effort we consider radiosondes launched in the proximity of the lidar site, as well as radiosondes launched from the closest radiosonde station included in the Integrated Global Radiosonde archive (IGRA). The inter-comparison effort considers data from October 2012. Results reveal a good agreement between the different approaches, with values of the correlation coefficient R2 in the range 0.52 to 0.94. Results clearly reveals that the combined application of different techniques to distinct sensors’ and model datasets allow getting accurate and cross-validated estimates of the ABL height over a variety of weather conditions. Furthermore, correlations between the ABL height and other atmospheric dynamic and thermodynamic variables as CAPE, friction velocity and relative humidity are also assessed to infer possible mutual dependences.

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC⁴A

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly 50 stations distributed over the Caribbean arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality integrated water vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the trade winds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD)-to-IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams International Airport (GAIA), Bridgetown, Barbados. A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2), where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a collocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth-generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS-derived IWV peaks. Two successive peaks are observed on 22 January and 23–24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organization for five representative GNSS stations across the Caribbean arc using visible satellite images. A statistically significant link was found between the cloud patterns and the local IWV observations from the GNSS sites as well as the larger-scale IWV patterns from the ECMWF ERA5 reanalysis. The reprocessed ZTD and IWV data sets from 49 GNSS stations used in this study are available from the French data and service centre for atmosphere (AERIS) (https://doi.org/10.25326/79; Bock, 2020b).

Precipitation modes over the Western Ghats orography during the summer monsoon season

<p>Precipitation distribution around an orographic barrier is controlled by the Froude Number (Fr) of the impinging flow. Fr is essentially a ratio of kinetic energy and stratification of winds around the orography. For Fr > 1 (Fr <1), the flow is unblocked (blocked) and precipitation occurs over the mountain peaks and the lee region (upwind region). While idealized modelling studies have robustly established this relationship, its widespread real-world application is hampered by the dearth of relevant observations. Nevertheless, the data collected in the field campaigns give us an opportunity to explore this relationship and provide a testbed for numerical models. A realistic distribution of precipitation over a mountainous region in these models is necessary for flash-flood and landslide forecasting. The Western Ghats region is a classic example where the orographically induced precipitation leads to floods and landslides during the summer monsoon season. In the recent INCOMPASS field campaign, it was shown that the precipitation over the west coast of India occurred in alternate offshore and onshore phases. The Western Ghats received precipitation predominantly during the onshore phase which was characterized by a stronger westerly flow. Here, using the radiosonde data from a station over the Indian west coast and IMERG precipitation product, we show that climatologically, these phases can be mapped over an Fr-based classification of the monsoonal westerly flow. Classifying the flow as 'High Fr' (Fr >1), 'Moderate Fr' ( 0.5 < Fr ≤ 1) and 'Low Fr' ( Fr ≤ 0.5 ) gives three topographical modes of precipitation -- 'Orographic', 'Coastal' and 'Offshore', respectively.  Moreover, these modes are not sensitive to the choice of radiosonde station over the west coast.</p>

IWV observations from a network of ground-based GNSS receivers during EUREC4A

<p>IWV data were retrieved from a network of nearly fifty Global Navigation Satellite System (GNSS) stations distributed over the Caribbean arc for the period 1 January-29 February 2020 encompassing the EUREC4A field campaign. Two of the stations had been installed at the Barbados Cloud Observatory (BCO) during fall 2019 in the framework of the project and are still running. All other stations are permanent stations operated routinely from various geodetic and geophysical organisations in the region. High spatial and temporal Integrated Water Vapour (IWV) observations will be used to investigate the atmospheric environment during the life cycle of convection and its feedback on the large-scale circulation and energy budget.</p><p>This paper describes the ground-based GNSS data processing details and assesses the quality of the GNSS IWV retrievals as well as the IWV estimates from radiosoundings, microwave radiometer measurements and ERA5 reanalysis.</p><p>The GNSS results from five different processing streams run by IGN and ENSTA-B/IPGP are first intercompared. Four of the streams were run operationally, among one was in near-real time, and one was run after the campaign in a reprocessing mode. The uncertainties associated with each of the data sets, including the zenith tropospheric delay to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the BCO and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (-2.9 kg/m2) and the GNSS results (-1.2 kg/m2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg/m2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The HATPRO IWV estimates agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to GNSS and BCO sondes before that date. ERA5 IWV estimates are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated.</p>

IWV observations in the Caribbean Arc from a network of ground-based GNSS receivers during EUREC⁴A

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)).

ATMOSPHERIC WEIGHTED MEAN TEMPERATURE MODEL IN GUILIN

Atmospheric water vapor is an important part of the earth's atmosphere, and it has a great relationship with the formation of precipitation and climate change. In CNSS-derived precipitable water vapor (PWV), atmospheric weighted mean temperature, Tm, is the key factor in the progress of retrieving PWV. In this study, using the profiles of Guilin radiosonde station in 2017, the spatiotemporal variation characteristics and relationships between Tm and surface temperature (Ts) are analyzed in Guilin, an empirical Tm model suitable for Guilin is constructed by regression analysis. Comparing the Tm values calculated from Bevis model, Li Jianguo model and new model, it is found that the root mean square error (RMSE) of new model is 2.349 K, which are decreased by 14% and 19%, respectively. Investigating the impact of different Tm models on GNSS-PWV, the Tm-induced error from new model has a smaller impact on PWV than other two models. The results show that the new Tm model in Guilin has a relatively good performance and it can improve the reliability of the regional GNSS water vapor retrieval to some extent.