A hybrid model for nowcasting of storms has been developed by employing predicted integrated water vapor (IWV) with light gradient boosting machine (GBM) on Global Navigation Satellite System (GNSS) receiver data collected at Gadanki (13.46°N, 79.17°E) and estimated thresholds for three storm predictors. The utilization of predicted IWV allows more lead time for disaster preparedness. The efficacy of light GBM technique in predicting IWV has been tested on 54 stormy days (from the year 2020), identified with a collocated polarimetric weather radar observations. The predicted IWV agrees very well with observed IWV with rms errors0.85) for predictions with a lead time up to 2 h. Among several predictors considered for nowcasting, IWV is found to have a great predictive potential, as the moisture buildup is seen few hours (1–4 h) prior to the occurrence of storm/rainfall. Thresholds for chosen predictors [magnitude of IWV, change in IWV, and change in brightness temperature (Tb)] are finalized using the data from known stormy days (65 days from the years 2018 and 2019). The sensitivity analysis of the predictors independently and in combination in predicting storms reveals that 1 and 2 parameter-based predictions detect storms accurately but produce large false alarm rates. The three-parameter scheme reduced the false alarm rate drastically to 5% and improved the model accuracy to 97%, which is much better than the existing methods.

NASA recently chose the Investigation into Convective Updrafts (InCUs) proposal as the next Earth Ventures program mission. INCUS will use a convoy of three identical Ka-band radars measuring radar reflectivity within their common swath to infer the characteristics of any convective updrafts that they observe. We summarize the theoretical basis for this approach, with justification from ground-based zenith profiler data as well as sensitivity analyses of convection-permitting simulations. We then describe and quantify the performance of the approach to detect updrafts from the radar observations. Finally, we illustrate the expected performance of retrievals of vertical transport, and evaluate their ability to meet the objectives of the INCUS mission. How this observation strategy can be adapted to miniaturized passive mm-wave radiometers is also discussed.

Long-term measurements of GNSS receiver at Gadanki, India, have been used to develop a machine learning technique – lightGBM for the prediction of integrated water vapor (IWV) with different lead times. A variety of data sets related to IWV (representing source, sink and transport) and short-scale features of IWV (gradients, sinusoidal pattern) have been used to train the model. Model performance is validated in different seasons and also on storm days. The predicted IWV at different lead times (30–120 min) perfectly captures the temporal variability of measured IWV with a correlation coefficient >0.99. The RMSE of predicted IWV with 30 minutes lead time is less than 1 mm in all seasons. Nevertheless, the RMSE for predicted IWV with longer lead times increases with lead time but always remains <3 mm. The bias is slightly larger during the monsoon, mainly due to the higher occurrence of longer duration rainy events. Even in those days, the model is able to accurately predict the enhanced IWVbefore the rain occurrence. Sensitivity analysis and feature importance analysis on different predictors used in the model reveal that the IWV features are more important for short-scale prediction, like 30 min, whereas the importance of other predictors is high for longer lead time prediction (1-2 hours) and on storm days.

The quality of the total column water vapor (TCWV) data retrieved from the Indian national satellite (INSAT) system series (INSAT-3D) and its variation at different temporal scales have been evaluated. The reference TCWV dataset is obtained from four ground-based global navigation satellite system (GNSS) receivers and over the entire Indian subcontinent from a global precipitation measurement (GPM) microwave imager (GMI) that uses global analysis (GANAL) model data. TCWV comparison of INSAT-3D, GPM-GMI with GNSS show higher correlations for GPM-GMI than INSAT-3D at all temporal scales. Though GMI can reproduce observed TCWV variations better than the INSAT-3D, the large biases of both the data sets indicate errors in the magnitude. Seasonal and monthly comparisons at two locations in the southeast peninsular India region show large correlations and small bias in the northeast and small correlations and large biases in southwest monsoon months. Southeast peninsular India receives a significant amount of rainfall in the northeast monsoon, and large TCWV correlations indicate both the algorithms removing cloud pixels with high accuracy. Therefore, the errors in the TCWV are attributed to the estimated radiances at different spectral channels. The spatiotemporal variations of TCWV correlations and bias of INSAT-3D indicate the need for improving the data quality by validating the estimated radiances at different spectral channels and, in turn, the retrieved TCWV over various regions at different temporal scales.

The turbulent kinetic energy (TKE) per unit mass estimated from the spectral width of very high frequency/ultra high frequency (VHF/UHF) radars often produce unrealistic negative values, particularly in the presence of strong winds, due to excess correction of nonturbulent factors. An experiment has been conducted with the newly acquired capabilities of the Advanced Indian MST Radar (AIR), in which several antenna configurations, providing different beamwidths, have been employed to evaluate various existing theoretical formulations for the estimation of nonturbulent factors. These formulations include traditional single-beamwidth, symmetric dual-beamwidth, and asymmetric beamwidth methods. The large variation is seen in the observed spectral widths with different antenna apertures with broader beams showing larger biases due to beam and shear broadening even in moderate winds. Wind-driven biases in spectral width are found to be larger for beams pointed perpendicular to the wind direction than those pointed parallel, particularly at heights of strong wind. After employing the above-mentioned correction methods, all profiles of mean spectral widths obtained with different antenna configurations converge and produce nearly equal values, indicating that the corrected spectral width ($\sigma ^{2}_{\mathrm{ turb}}$) values may represent realistic estimates of turbulence intensity. Comparison of$\sigma ^{2}_{\mathrm{ turb}}$estimated using east and south beams indicates that the turbulence (on average) is isotropic. Among all the correction methods, the$\sigma ^{2}_{\mathrm{ turb}}$obtained by employing the asymmetric dual-beamwidth method has relatively larger bias than other methods. The$\sigma ^{2}_{\mathrm{ turb}}$values are used to estimate the vertical eddy diffusion coefficients and eddy dissipation rates to have direct comparisons with those available in the literature.

The recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model.

This paper describes an automated data processing algorithm to identify the turbulence echo in the multipeaked very high-frequency radar spectra, generally observed during the precipitation. Although a few multipeak-picking algorithms exist, they identify the turbulence echo from the clutter and noise, but they do not distinguish turbulence and precipitation echoes. The present algorithm identifies the turbulence echo from multiple peaks based on several criteria, such as the significance of the echoes, separation between the echoes within a sliding window, and a reference turbulence echo. A new method [symmetric method (SM)] is introduced to estimate the moments after proper identification of the turbulence echo, in fact, half portion of the echo. Comparison of the moments estimated by the SM and dual-frequency method (another popular method for the extraction of moments from multipeaked spectra) with those obtained by the routine single-peak detection algorithm reveals large discrepancies, particularly in convective cores and in and above the radar bright band region. In these regions, the error in moments by the traditional single-peak-picking algorithm is found to be substantial. Performance evaluation of these two methods has also been carried out against simulated spectra. The analysis reveals that the error in obtaining moments by the SM and dual-frequency method is less than 15%, except for a few cases. Also, the error in moments by the SM is, in general, smaller than by the dual-frequency method.