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- Author or Editor: Siebren de Haan x
- Journal of Applied Meteorology and Climatology x
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Abstract
Nowcasting of convective systems plays a crucial role in weather forecasting. The strength of convection depends on the (in)stability of the air column. The stability can be detected by radiosonde observations. However, these observations are not frequent (typically 2 times per day) and are expensive to deploy. In this article a method is presented to detect the stability of the atmosphere based on high-frequency global positioning system (GPS) path-delay observations. The convection parameter derived from these observations is the power of the nonisotropic GPS path-delay signal. Comparisons with the convective available potential energy obtained from radiosonde observations show a correlation with the convection parameter obtained from GPS. This result implies that, because of the continuous availability of GPS estimates and the good land coverage, this method of detecting atmospheric stability may be beneficial to forecasters.
Abstract
Nowcasting of convective systems plays a crucial role in weather forecasting. The strength of convection depends on the (in)stability of the air column. The stability can be detected by radiosonde observations. However, these observations are not frequent (typically 2 times per day) and are expensive to deploy. In this article a method is presented to detect the stability of the atmosphere based on high-frequency global positioning system (GPS) path-delay observations. The convection parameter derived from these observations is the power of the nonisotropic GPS path-delay signal. Comparisons with the convective available potential energy obtained from radiosonde observations show a correlation with the convection parameter obtained from GPS. This result implies that, because of the continuous availability of GPS estimates and the good land coverage, this method of detecting atmospheric stability may be beneficial to forecasters.
Abstract
In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a two-dimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth–Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m−2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m−2 for the NWP analysis to 4 kg m−2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air.
Abstract
In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a two-dimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth–Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m−2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m−2 for the NWP analysis to 4 kg m−2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air.
Abstract
The use of integrated water vapor (IWV) measurements from a ground-based global positioning system (GPS) for nowcasting is described for a cold front that passed the Netherlands during 16 and 17 May 2000. Meteosat water vapor (WV) and infrared (IR) channel measurements are incorporated to analyze this weather situation. A cloud band with embedded cumulonimbus clouds (Cb) preceded the cold front. The GPS IWV showed a clear signal at the passing time of the embedded Cbs over the GPS sites. After the frontal passage a dry intrusion occurred. By comparing Meteosat WV observations collocated in time and space with GPS IWV observations, a rough reconstruction of the vertical water vapor distribution can be made. The case described here shows that, in addition to Meteosat WV/IR images, GPS IWV contained information for nowcasting of the probability of the occurrence of thunderstorms and heavy precipitation.
Abstract
The use of integrated water vapor (IWV) measurements from a ground-based global positioning system (GPS) for nowcasting is described for a cold front that passed the Netherlands during 16 and 17 May 2000. Meteosat water vapor (WV) and infrared (IR) channel measurements are incorporated to analyze this weather situation. A cloud band with embedded cumulonimbus clouds (Cb) preceded the cold front. The GPS IWV showed a clear signal at the passing time of the embedded Cbs over the GPS sites. After the frontal passage a dry intrusion occurred. By comparing Meteosat WV observations collocated in time and space with GPS IWV observations, a rough reconstruction of the vertical water vapor distribution can be made. The case described here shows that, in addition to Meteosat WV/IR images, GPS IWV contained information for nowcasting of the probability of the occurrence of thunderstorms and heavy precipitation.
Abstract
Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.
Abstract
Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.
