Investigating the Inter-Relationships among Multiple Atmospheric Variables and Their Responses to Precipitation

Abstract

In this study, a comprehensive investigation into the inter-relationships among twelve atmospheric variables and their responses to precipitation was conducted. These variables include two Global Navigation Satellite Systems (GNSS) tropospheric products, eight weather variables and two time-varying parameters. Their observations and corresponding precipitation record over the period 2008–2019 were obtained from a pair of GNSS/weather stations in Hong Kong. Firstly, based on the correlation and regression analyses, the cross-relationships among the variables were systematically analyzed. Typically, the variables of precipitable water vapor (PWV), zenith total delay (ZTD), temperature, pressure, wet-bulb temperature and dew-point temperature have closer cross-correlativity. Next, the responses of these variables to precipitation of different intensities were investigated and some precursory information of precipitation contained in these variables was revealed. The lead times of using ZTD and PWV to detect heavy precipitation are about 8 h. Finally, by using the principal component analysis, it is shown that heavy precipitation can be effectively detected using these variables, among which, ZTD, PWV and cloud coverage play more prominent roles. The research findings can not only increase the utilization and uptake of atmospheric variables in the detection of precipitation, but also provide clues in the development of more robust precipitation forecasting models.

Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic

“…Introducing a time lag in snowfall rate and IWV data does not significantly change the corresponding correlation coefficients. This is in line with the results obtained in [10] where only small changes in correlations between warm precipitation and IWV were found when time shifts of up to 12 h were considered.…”

“…The precipitation rate-columnar water vapor correlation coefficients found here for the Arctic are on the low side of the range of correlations between liquid precipitation and IWV found in [9] for different regions in China. These coefficients, however, are higher than a ~0.26 value found recently in [10] for the correlation between rain precipitation intensity and IWV. Introducing a time lag in snowfall rate and IWV data does not significantly change the corresponding correlation coefficients.…”

“…The corresponding correlation coefficients were smaller than ~0.3 for both the NSA and MOSAiC data sets. Note that the correlation coefficients between warm precipitation and temperature/humidity parameters in [10] were even lower (i.e., r < 0.1). The concurrent precipitation and surface meteorology observations indicated that NSA snowfall most often occurred when nearsurface temperatures were between approximately −15 • C and −10 • C while a significant part of the MOSAiC snowfall was observed at colder temperatures of around −25 • C. Near-surface relative humidity values during snowfall at both locations considered here were generally near saturation with respect to ice.…”

“…Most recently, Zhang et al [9] and Li at al. [10] conducted correlation analysis of IWV and precipitation over different regions in China. These authors found that the Pearson correlation coefficients between IWV and the observed precipitation amounts varied over an approximate range of between about 0.3 and 0.9 depending on the climatic zone.…”

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

“…To allow comparison with previous studies [11,17], the TDR and FFR are selected as evaluation indexes for the proposed rainfall forecast model. The specific calculation formulas for the TDR and FFR are as follows: (8) where N true refers to the correct number of model rainfall forecasts, N acutal refers to the number of actual occurrences of rainfall, and N f alse refers to the false number of model rainfall forecasts.…”