Petr Zacharov

Bio: Petr Zacharov is an academic researcher from Academy of Sciences of the Czech Republic. The author has contributed to research in topics: Numerical weather prediction & Quantitative precipitation forecast. The author has an hindex of 9, co-authored 14 publications receiving 162 citations.

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

Abstract: Two methods for assimilating radar reflectivity into the COSMO numerical weather prediction (NWP) model were compared to precipitation forecasts. The first method assimilated observed radar reflectivity, and the second one assimilated observed and extrapolated radar reflectivity. The assimilation technique was based on the correction of the model's water vapour mixing ratio. The extrapolation was performed by the COTREC method and was 1 hour long. The model's horizontal resolution was 2.8 km. The comparison of methods was based on verification of the observed and forecast hourly precipitation. The comparison was performed for the 1st, 2nd and 3rd hours of each forecast. On the whole, 45 forecasts from nine days of convective precipitation were evaluated for each hour. The evaluation included subjective verification and the following objective skill scores: Fractions Skill Scores, SAL and a measure based on a categorical-probabilistic approach. The results confirmed that assimilation complemented by the extrapolated data improves the accuracy of precipitation forecasts. The improvement was obvious in a majority of the single forecasts studied, and it is confirmed by all evaluation techniques. COSMO forecasts that used the extrapolation showed reasonable competence in forecasting for the first and the second hours. Copyright © 2011 Royal Meteorological Society

Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges

Abstract: Traditionally, the nowcasting of precipitation was conducted to a large extent by means of extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of traditional extrapolation-based techniques with high-resolution numerical weather prediction (NWP) is gaining popularity in the nowcasting community. The increased need of NWP products in nowcasting applications poses great challenges to the NWP community because the nowcasting application of high-resolution NWP has higher requirements on the quality and content of the initial conditions compared to longer-range NWP. Considerable progress has been made in the use of NWP for nowcasting thanks to the increase in computational resources, advancement of high-resolution data assimilation techniques, and improvement of convective-permitting numerical modeling. This paper summarizes the recent progress and discusses some of the challenges for future advancement.

A Review of High Impact Weather for Aviation Meteorology

Abstract: This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Abstract: Author(s): Akbari Asanjan, A; Yang, T; Hsu, K; Sorooshian, S; Lin, J; Peng, Q | Abstract: Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6nhr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.

Progress and challenges in forecast verification

Abstract: Verification scientists and practitioners came together at the 5th International Verification Methods Workshop in Melbourne, Australia, in December 2011 to discuss methods for evaluating forecasts within a wide variety of applications. Progress has been made in many areas including improved verification reporting, wider use of diagnostic verification, development of new scores and techniques for difficult problems, and evaluation of forecasts for applications using meteorological information. There are many interesting challenges, particularly the improvement of methods to verify high resolution ensemble forecasts, seamless predictions spanning multiple spatial and temporal scales, and multivariate forecasts. Greater efforts are needed to make best use of new observations, forge greater links between data assimilation and verification, and develop better and more intuitive forecast verification products for end-users. Copyright © 2013 Royal Meteorological Society