Mean Temperature Model Research Articles

Optimised weighted mean temperature model based on generalised regression neural network

Optimised weighted mean temperature model based on generalised regression neural network

A weighted mean temperature model using principal component analysis for Greenland

A weighted mean temperature model using principal component analysis for Greenland

Development of an empirical model for weighted mean temperature over North Indian region

Weighted mean temperature ( T m ) is an important parameter in the process of GPS precipitable water vapour (PWV) estimation. GPS PWV is obtained by multiplying a proportionality constant in zenith wet delay (ZWD). This proportionality constant depends on parameter T m . The parameter T m depends on surface temperature which varies from one place to another. When this variation is large, a more specific T m is required for a region for accurate PWV estimation. The topography of the northern Indian region is quite complex and the Himalayan mountains play a vital role in several atmospheric processes in this region. Thus a regional T m model is required for this region which should be developed with observational input over the foothills of northwestern Himalayas. In the present study, a regional weighted mean temperature model is developed over this region using five years of radiosonde observations from 2016 to 2020 over five stations New Delhi, Patiala, Dehradun, Jammu, and Srinagar. Site-specific models are also developed over these locations. Their accuracies are assessed with respect to existing site-specific, regional and global models and radiosonde observations. The results show that differences between developed regional model and existing regional and global linear models are less than 3 °K. The developed regional and site specific models have the T m differences of the order of 2 °K to 3 °K which is less than the difference between other models and radiosonde observations. Site-specific models provide slightly better accuracies (less than 1°K) when compared with the developed regional model. PWV is estimated using both the models and an average difference of less than 1 mm is found when compared with radiosonde observations. The developed regional model has better PWV accuracy than any other existing model in this region. Hence, both site-specific and regional models developed in this study can be used for further PWV estimation in the north Indian region.

The influence of rainfall measurement on the associations between rainfall and acute gastro-intestinal illness in North Carolina

BACKGROUND AND AIM: Climate change and extreme weather are predicted to have a significant impact on diarrheal diseases. However, rainfall depends on local conditions and studies use varied measurements (e.g., absolute rainfall, heavy rainfall, or antecedent conditions) to define exposures. We systematically explored the influence of different rainfall measures on the association between acute gastrointestinal illness (AGI) and rainfall in North Carolina. METHODS: Common measures of rainfall were derived from recent studies (e.g., absolute rainfall (mm); heavy rainfall (90th, 95th, 99th percentile) and constructed from PRISM gridded daily weather data spatially aggregated to the ZIP code level. Rate ratios between rainfall (lagged 0-7 days) and AGI were estimated using quasi-Poisson time series models. All-cause AGI was defined by the daily emergency department (ED) visits per ZIP code using ICD-9 diagnosis codes from North Carolina’s syndromic surveillance system (2008-2015). Unadjusted and adjusted (for mean temperature and relative humidity) model estimates and goodness-of-fit measures were compared across rainfall measures. RESULTS: Between 2008-2015, there were 1.07M ZIP code-days with at least one ED visits for all-cause AGI per ZIP code per day in North Carolina. Adjusting for lagged mean temperature and relative humidity, we observed a 4.2% increase (RR=1.035; 95% CI: 1.029-1.040) and 5.1% (1.051; 1.035-1.067) in AGI ED visits respectively following lagged 90th and 99th percentile rainfall (1 day lag), with similar patterns for lags 2-7. By contrast, a 0.1% change in AGI ED visits was associated with a 1 mm increase in rainfall depending on the lag (e.g., lag 1: 1.001-1.001, 1.002 vs. lag 7: 0.999; 0.999-1.000). CONCLUSIONS: Heavy rainfall was associated with an increase in AGI rates, but absolute rainfall had a much smaller and ambiguous effect. These results suggest the importance of rainfall measurement and adjustment for other weather variables. KEYWORDS: diarrhea, rainfall, weather, climate, time series, exposure measurement

Random Forest-Based Model for Estimating Weighted Mean Temperature in Mainland China

The weighted mean temperature (Tm) is a vital parameter for converting zenith wet delay (ZWD) into precipitation water vapor (PWV) and plays an essential part in the Global Navigation Satellite System (GNSS) inversion of PWV. To address the inability of current mainstream models to fit the nonlinear relationship between Tm and meteorological and spatiotemporal factors, whose accuracy is limited, a weighted mean temperature model using the random forest (named RFTm) was proposed to enhance the accuracy of the Tm predictions in mainland China. The validation with the Tm from 84 radiosonde stations in 2018 showed that the root mean square (RMS) of the RFTm model was reduced by 38.8%, 44.7%, and 35.5% relative to the widely used Global Pressure and Temperature 3 (GPT3) with 1° × 1°/5° × 5° versions and Bevis, respectively. The Bias and RMS of the new model in different latitude bands, various height intervals, and different times were significantly better than those of the other three comparative models. The accuracy of the new model presented a more stable adaptability. Therefore, this study provides a new idea for estimating Tm and can provide a more accurate Tm for GNSS meteorology.

Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval

AbstractThe atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σ pwv and σ pwv /PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research.

A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value.

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.

A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM

In the meteorology of Global Navigation Satellite System, the weighted mean temperature (Tm) is a key parameter in the process of converting the zenith wetness delay into precipitable water vapor, and it plays an important role in water vapor monitoring. In this research, two deep learning algorithms, namely, recurrent neural network (RNN) and long short-term memory neural network (LSTM), were used to build a high-precision weighted mean temperature model for China using their excellent time series memory capability. The model needs site location information and measured surface temperature to predict the weighted mean temperature. We used data from 118 stations in and around China provided by the Integrated Global Radiosonde Archive from 2010 to 2015 to train the model and data from 2016 for model testing. The root mean square error (RMSE) of the RNN_Tm and LSTM_Tm models were 3.01 K and 2.89 K, respectively. Compared with the values calculated by the empirical GPT3 model, the accuracy was improved by 31.1% (RNN_Tm) and 33.9% (LSTM_Tm). In addition, we selected another 10 evenly distributed stations in China and used the constructed model to test the prediction capability of the weighted mean temperature from 2010 to 2016. The RMSE values were 2.95 K and 2.86 K, which proved that the model also exhibits high generalization in non-modeling sites in China. In general, the RNN_Tm and LSTM_Tm models have a good performance in weighted mean temperature prediction.

A new global grid-based weighted mean temperature model considering vertical nonlinear variation

Abstract. Global navigation satellite systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (Tm) along the vertical direction in the atmosphere over the site. Thus, the accuracy of Tm directly affects the quality of the GNSS-derived PWV. Currently, the Tm value at a target height level is commonly modeled using the Tm value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in Tm is neglected. This may result in large errors in the Tm result for the target height level, as the variation trend in the vertical direction of Tm may not be linear. In this research, a new global grid-based Tm empirical model with a horizontal resolution of 1∘ × 1∘ , named GGNTm, was constructed using ECMWF ERA5 monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in Tm at the grid points, and the temporal variation in each of the four coefficients in the Tm fitting function was also modeled with the variables of the mean, annual, and semi-annual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted Tm values in 2018 to compare with the following two references in the same year: (1) Tm from ERA5 hourly reanalysis with the horizontal resolution of 5∘ × 5∘; (2) Tm from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted Tm values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted Tm values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model's Tm values over all pressure levels in the height range from the surface to 10 km altitude were −0.16 and 4.20 K, respectively. The new model results were also compared with that of the GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in Tm and the temporal variation in the coefficients of the Tm model can significantly improve the accuracy of model-predicted Tm for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals.

An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate

Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV.

Analysis and Comparison of GPS Precipitable Water Estimates between Two Nearby Stations on Tahiti Island.

Since Bevis first proposed Global Positioning System (GPS) meteorology in 1992, the precipitable water (PW) estimates retrieved from Global Navigation Satellite System (GNSS) networks with high accuracy have been widely used in many meteorological applications. The proper estimation of GNSS PW can be affected by the GNSS processing strategy as well as the local geographical properties of GNSS sites. To better understand the impact of these factors, we compare PW estimates from two nearby permanent GPS stations (THTI and FAA1) in the tropical Tahiti Island, a basalt shield volcano located in the South Pacific, with a mean slope of 8% and a diameter of 30 km. The altitude difference between the two stations is 86.14 m, and their horizontal distance difference is 2.56 km. In this paper, Bernese GNSS Software Version 5.2 with precise point positioning (PPP) and Vienna mapping function 1 (VMF1) was applied to estimate the zenith tropospheric delay (ZTD), which was compared with the International GNSS Service (IGS) Final products. The meteorological parameters sourced from the European Center for Medium-Range Weather Forecasts (ECMWF) and the local weighted mean temperature () model were used to estimate the GPS PW for three years (May 2016 to April 2019). The results show that the differences of PW between two nearby GPS stations is nearly a constant with value 1.73 mm. In our case, this difference is mainly driven by insolation differences, the difference in altitude and the wind being only second factors.

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.

A Real-Time Precipitable Water Vapor Monitoring System Using the National GNSS Network of China: Method and Preliminary Results

The development of the International Global Navigation Satellite System (GNSS) Service (IGS) Real-Time Pilot Project shows promise for real-time GNSS-based precipitable water vapor (GNSS-PWV) retrieval. For better applications of real-time GNSS meteorology over China, a method is proposed to establish a real-time GNSS-PWV monitoring system using the national GNSS network of China. The prototype system generating the zenith tropospheric delay (ZTD) is based on the real-time precise point positioning technique, in which the real-time data streams and state-space-representative satellite orbit and clock corrections are processed. The parallel computing technology is embedded in the system for decoding and processing the real-time data streams, which enables the multistation processing mode and improves the computing efficiency. At the initial phase, a total of 215 global positioning system (GPS) stations from the crustal movement observation network of China are used to generate the real-time GPS-ZTDs. The pressure data for separating the zenith wet delay (ZWD) from the ZTD are obtained from the collocated meteorological sensors. An advanced weighted mean temperature model, namely, Gridded-Mixed Tm , is adopted in the system to determine the conversion factor “Π” (converting GPS-ZWD to GPS-PWV) in real-time mode with the measured temperatures input. The generated real-time GPS-PWV products have a time resolution of 5 min. To validate the system performance, comparisons between the real-time GPS-PWV and the PWVs derived from nearby radiosonde data (RS-PWV) and reanalysis data from the National Centers for Environmental Prediction (NCEP)-Department of Energy Reanalysis II (NCEP-II-PWV) are conducted over a period of 83 days. The comparisons show a mean bias of −0.1 mm with a root mean square (RMS) of 1.7 mm between the real-time GPS-PWV and RS-PWV. The agreement between the real-time GPS-PWV and NCEP-II-PWV is approximately 2.0 mm in terms of RMS and has a mean bias of −0.8 mm. These results confirm that the established system can be used for real-time PWV monitoring across China.

An improved atmospheric weighted mean temperature model and its impact on GNSS precipitable water vapor estimates for China

The atmospheric weighted mean temperature, $${T_{\text{m}}}$$Tm, is an important parameter for retrieving precipitable water vapor (PWV) from global navigation satellite system (GNSS) signals. There are few empirical, high-precision $${T_{\text{m}}}$$Tm models for China, which limit the real-time and high-precision application of GNSS meteorology over China. The GPT2w (Global Pressure and Temperature 2 Wet) model, as a state-of-the-art global empirical tropospheric delay model, can provide values for $${T_{\text{m}}}$$Tm, surface temperature, surface pressure, and water vapor pressure. However, several studies have noted that the GPT2w model has significant systematic errors in the calculation of $${T_{\text{m}}}$$Tm for China, mainly due to the neglect of the $${T_{\text{m}}}$$Tm lapse rate. We develop an improved $${T_{\text{m}}}$$Tm model for China, IGPT2w, by refining the $${T_{\text{m}}}$$Tm derived from GPT2w using both gridded $${T_{\text{m}}}$$Tm data and ellipsoidal height grid data from the Global Geodetic Observing System (GGOS) Atmosphere. Both gridded $${T_{\text{m}}}$$Tm data from the GGOS Atmosphere and radiosonde data from 2015 are used to test the performance of IGPT2w in China. The results are compared with the GPT2w model and the widely used Bevis formula. The results show that IGPT2w yields significant performance against other models in $${T_{\text{m}}}$$Tm estimation over China, especially in western China, where the significant systematic errors of the GPT2w model are largely eradicated. IGPT2w has $${\sigma _{{\text{PWV}}}}$$?PWV and $${\sigma _{{\text{PWV}}}}/{\text{PWV}}$$?PWV/PWV values of 0.29 mm and 1.38% when used to retrieve GNSS-PWV, respectively. Thus, the IGPT2w has significant potential for real-time GNSS-PWV sounding in China, especially when used to retrieve GNSS-PWV values for the study of PWV transportation in the Tibetan Plateau.

A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map.

The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.

Climate Sensitivity and the Responsiveness of Temperature to Atmospheric CO2

Detrended correlation analysis of global mean temperature observations and model projections are compared in a test for the theory that surface temperature is responsive to atmospheric CO2 concentration in terms of GHG forcing of surface temperature implied by the Climate Sensitivity parameter ECS. The test shows strong evidence of GHG forcing of warming in the theoretical RCP8.5 temperature projections made with CMIP5 forcings. However, no evidence of GHG forcing by CO2 is found in observational temperatures from four sources including two from satellite measurements. The test period is set to 1979-2018 so that satellite data can be included on a comparable basis. No empirical evidence is found in these data for a climate sensitivity parameter that determines surface temperature according to atmospheric CO2 concentration or for the proposition that reductions in fossil fuel emissions will moderate the rate of warming.

A global weighted mean temperature model based on empirical orthogonal function analysis

A global empirical orthogonal function (EOF) model of the tropospheric weighted mean temperature called GEOFM_Tm was developed using high-precision Global Geodetic Observing System (GGOS) Atmosphere Tm data during the years 2008–2014. Due to the quick convergence of EOF decomposition, it is possible to use the first four EOF series, which consists base functions Uk and associated coefficients Pk, to represent 99.99% of the overall variance of the original data sets and its spatial-temporal variations. Results show that U1 displays a prominent latitude distribution profile with positive peaks located at low latitude region. U2 manifests an asymmetric pattern that positive values occurred over 30° in the Northern Hemisphere, and negative values were observed at other regions. U3 and U4 displayed significant anomalies in Tibet and North America, respectively. Annual variation is the major component of the first and second associated coefficients P1 and P2, whereas P3 and P4 mainly reflects both annual and semi-annual variation components. Furthermore, the performance of constructed GEOFM_Tm was validated by comparison with GTm_III and GTm_N with different kinds of data including GGOS Atmosphere Tm data in 2015 and radiosonde data from Integrated Global Radiosonde Archive (IGRA) in 2014. Generally speaking, GEOFM_Tm can achieve the same accuracy and reliability as GTm_III and GTm_N models in a global scale, even has improved in the Antarctic and Greenland regions. The MAE and RMS of GEOFM_Tm tend to be 2.49 K and 3.14 K with respect to GGOS Tm data, respectively; and 3.38 K and 4.23 K with respect to IGRA sounding data, respectively. In addition, those three models have higher precision at low latitude than middle and high latitude regions. The magnitude of Tm remains at the range of 220–300 K, presented a high correlation with geographic latitude. In the Northern Hemisphere, there was a significant enhancement at high latitude region reaching 270 K during summer. GEOFM_Tm is capable to represent the spatiotemporal variations of Tm, with the high accuracy and reliability in a global scale, therefore, will be of great significance to the real-time GNSS water vapor inversion and climate studies.

A new weighted mean temperature model in China

The Global Positioning System (GPS) has been applied in meteorology to monitor the change of Precipitable Water Vapor (PWV) in atmosphere, transformed from Zenith Wet Delay (ZWD). A key factor in converting the ZWD into the PWV is the weighted mean temperature (Tm), which has a direct impact on the accuracy of the transformation. A number of Bevis-type models, like Tm-Ts and Tm-Ts,Ps type models, have been developed by statistics approaches, and are not able to clearly depict the relationship between Tm and the surface temperature, Ts. A new model for Tm, called weighted mean temperature norm model (abbreviated as norm model), is derived as a function of Ts, the lapse rate of temperature, δ, the tropopause height, htrop, and the radiosonde station height, hs. It is found that Tm is better related to Ts through an intermediate temperature. The small effects of lapse rate can be ignored and the tropopause height be obtained from an empirical model. Then the norm model is reduced to a simplified form, which causes fewer loss of accuracy and needs two inputs, Ts and hs. In site-specific fittings, the norm model performs much better, with RMS values reduced averagely by 0.45K and the Mean of Absolute Differences (MAD) values by 0.2K. The norm model is also found more appropriate than the linear models to fit Tm in a large area, not only with the RMS value reduced from 4.3K to 3.80K, correlation coefficient R2 increased from 0.84 to 0.88, and MAD decreased from 3.24K to 2.90K, but also with the distribution of simplified model values to be more reasonable. The RMS and MAD values of the differences between reference and computed PWVs are reduced by on average 16.3% and 14.27%, respectively, when using the new norm models instead of the linear model.

MODELLING OF HEAT AND MASS TRANSFER PROCESSES IN PHASE TRANSFORMATION CYCLE OF SPRAYED WATER INTO GAS 5. NUMERICAL MODELLING OPTIMIZATION OF PHASE TRANSFORMATION CYCLE FOR DROPLETS SLIPPING IN GAS FLOW

Condensing and transit evaporation regimes for phase transformation cycle were modelled for slipping droplets in humid gas. By concluded results of droplet thermal, energetic, dynamics and phase transformation parameters P it is based on that according to unit initial Reynolds number grouped droplet cloud, universality of dimensionless transfer parameters PFo functions graphs is valid in aspect of droplet dispersity for cases defined by water and gas initial temperature and humidity. According to phase transformation cycle modelling results for intensively slipping droplet by conditions 0 Re  50 it was received that by "c" heat transfer model calculated droplet moment mass mean temperature m,"c" T can exceed up to 2.5 K temperature m,"k " T calculated according to "k" heat transfer model. An applied droplet mass mean temperature model can lead to ± 0.05 percent imbalance for calculated droplet surface area.DOI: http://dx.doi.org/10.5755/j01.mech.23.1.13689