Using a cost-effective approach to increase background ensemble member size within the GSI-based EnVar system for improved radar analyses and forecasts of convective systems

A Valid Time Shifting (VTS) method is explored for the GSI-based ensemble variational (EnVar) system modified to directly assimilate radar reflectivity at convective scales. VTS is a cost-efficient method to increase ensemble size by including subensembles before and after the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined here for assimilating radar reflectivity in a continuous hourly analysis system for a case study of 1-2 May 2019. The VTS implementation is compared against a 36-member control experiment (ENS-36), to increase ensemble size (3×36 VTS), and as a cost-savings method (3×12 VTS), with time-shifting intervals τ between 15 and 120 min. The 3×36 VTS experiments increased the ensemble spread, with largest subjective benefits in early cycle analyses during convective development. The 3×12 VTS experiments captured analysis with similar accuracy as ENS-36 by the third hourly analysis. Control forecasts launched from hourly EnVar analyses show significant skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3×36 VTS experiments, subjectively attributable to more accurate placement of the convective line. For 3×12 VTS, experiments with τ ≥ 60 min met and exceeded the skill of ENS-36 out to forecast hour 15, with VTS-3×12τ90 maximizing skill. Sensitivity results demonstrate preference to τ = 30–60 min for 3x36 VTS and 60 – 120 min for 3×12 VTS. The best 3×36 VTS experiments add a computational cost of 45-67%, compared to the near tripling of costs when directly increasing ensemble size, while best 3×12 VTS experiments save about 24-41% costs over ENS-36.

Track, intensity and few dynamical aspects of ‘AILA’ as simulated by operational NWP model of the IAF

bl 'kks/k i= esa Hkkjrh; ok;q lsuk ds izpkyukRed ,u- MCY;w- ih- ekWMy dh {kerk dk ebZ 2009 esa caxky dh [kkM+h esa cus vkSj if’pe caxky dks izHkkfor djus okys izpaM pØokrh; rwQku ^vk;yk^ ds dqN xR;kRed igyqvksa] mlds ekxZ] rhozrk ,oa LFky izos’k ds iwokZuqeku dk fo’ys"k.k djus dk iz;kl fd;k x;k gSA bl ekWMy dks 6 fd- eh- ds NksVs Hkkx esa diklh izkpyhdj.k ;kstuk ds lkFk vkSj mlds fcuk bl ;kstuk dk iz;ksx djds ns[kk x;k gSA ckjh ckjh ls fd, x, nksuksa iz;ksxksa ds lsV esa Mh&2 ij rS;kj fd, x, iwokZuqeku dh rqyuk esa Mh&1 ds iwokZuqeku vis{kk—r csgrj vkSj vkf/kd lgh ik, x,A diklh izkpyhdj.k ds iSVUlZ foyac ls laogu iSnk djrs gSa ijUrq bl iSVUlZ ds fcuk rS;kj fd, ifj.kke dh rqyuk esa vf/kd lgh gSA 6 fd-eh- ds NksVs Hkkx esa diklh izkpyhdj.k ;kstuk ds fcuk jsMkj dh ijkofrZrk Mh- MCY;w- vkj- dksydkrk ds okLrfod ijkofrZrk le; vkSj LFkku nksuksa dh rqyuk esa vf/kdre ns[kh xbZ gSA An attempt has been made in this study to analyse the efficacy of operational NWP Model of the IAF in predicting the track, intensity, landfall and few dynamical aspects of ‘AILA’ a Severe Cyclonic Storm that formed over the Bay of Bengal and affected West Bengal during May 2009. Model runs were done with and without employment of cumulus parameterisation scheme in the finer domain of 6 km. The forecasts of D-1 were relatively better and more realistic in comparison to the one generated on D-2, in both sets of experiment, respectively. Patterns with cumulus parameterisation produced delayed convection but with finer details in comparison to the patterns generated without it. Maximum radar reflectivity without using cumulus parameterisation scheme in the finer domain of 6 km, compared well with the actual reflectivity of Kolkata DWR both in time and space.

An AI-Based Query System for Historical Weather Radar Reflectivity Retrieval Using a Convolutional Auto-encoder

The weather patterns in Taiwan have been more changeable in recent years. Taking rainfall as an example, natural disasters that lead to major damage are often caused by heavy rainfall in a short period of time. Therefore, how to effectively predict changes in weather patterns has become an important issue for the development of disaster prevention technology. In this paper, an AI-based query system using a convolutional auto-encoder is developed for historical weather radar reflectivity retrieval that is expected to forecast heavy rainfall soon. When the weather radar receives a current radar reflectivity, meteorologists can employ the query system to quickly search for the historical radar reflectivity with the weather characteristics similar to the one to be queried. The experimental results reveal that under the given MSE threshold of 58,000, the average pass rate achieves 83.9% for the ranked top 10 similar results from 33,461 historical records via 4,319 query tests in total. Without considering the model loading time of 60 seconds, the execution time of each query requires 6 seconds for only listing query results or 8 seconds for including saving resulting maps in pseudo-colour.

Raindrop Size Distribution and Rain Characteristics of the 2017 Great Hunan Flood Observed with a Parsivel2 Disdrometer

Disdrometer observations obtained by an OTT Parsivel2 during the 2017 Great Hunan Flood from 1:00 a.m. LST 23 June 2017 to 4:00 a.m. LST 2 July 2017 in Changsha, Hunan Province, southern China, are analyzed to diagnose characteristics of raindrop size distribution (DSD). This event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) and the mean median volume diameter (D0) is about 1.04 mm. The median values of rain rate R (1.57 mm h−1), liquid water content W (0.10 g m−3), and radar reflectivity Z (25.7 dBZ) are smaller than that of the 2013 Great Colorado Flood. This event was composed of two intense rainfall periods and a stratiform period, and notable distinctions of rainfall microphysics among the three rainfall episodes are observed. Two intense rainfall periods were characterized by widespread and intense convection rains with a surface reflectivity of 48.8~56.7 dBZ. A maximum diameter of raindrops up to 7.5 mm was observed, as well as high concentrations of small and midsize drops, resulting in large rainfall amounts during the two intense rainfall episodes. The mean radar reflectivity of 22.6 dBZ, total rainfall of 17.85 mm and the maximum raindrop of approximately 4.25 mm were observed during the stratiform rainfall episode. The composite DSD for each rainfall episode peaked at 0.56 mm but higher concentrations of raindrops appeared in the two intense rainfall episodes. The Z-R relationships derived from the disdrometer measurements reflect the unusual characteristics of DSD during the flood. As a result, the standard NEXRAD Z-R relationship (Z = 300R1.4) strongly underestimated hourly rainfall by up to 27.5%. In addition, the empirical relations between rainfall kinetic energy (KE) versus rainfall intensity (R) and mean mass diameter (Dm) are also derived using DSDs to further investigate the impacts of raindrop properties on the rainfall erosivity.

Microphysical processes producing high ice water contents (HIWCs) in tropical convective clouds during the HAIC-HIWC field campaign: dominant role of secondary ice production

High ice water content (HIWC) regions in tropical deep convective clouds, composed of high concentrations of small ice crystals, were not reproduced by Weather Research and Forecasting (WRF) model simulations at 1-km horizontal grid spacing using four different bulk microphysics schemes (i.e., the WRF single‐moment 6‐class microphysics scheme (WSM6), the Morrison scheme and the Predicted Particle Properties (P3) scheme with one- and two-ice options) for conditions encountered during the High Altitude Ice Crystals (HAIC)-HIWC experiment. Instead, overestimates of radar reflectivity and underestimates of ice number concentrations were realized. To explore formation mechanisms for large numbers of small ice crystals in tropical convection, a series of quasi-idealized WRF simulations varying the model resolution, aerosol profile, and representation of secondary ice production (SIP) processes are conducted based on an observed radiosonde released at Cayenne during the HAIC-HIWC field campaign. The P3 two-ice scheme, which has two “free” ice categories to represent all ice-phase hydrometeors, is used. Regardless of the horizontal grid spacing or aerosol profile used, without including SIP processes the model produces total ice number concentrations about two orders of magnitude less than observed at −10 °C and about an order of magnitude less than observed at −30 °C, but slightly overestimates the total ice number concentrations at −45 °C. Three simulations including one of three SIP mechanisms separately (i.e., the Hallett-Mossop mechanism, fragmentation during ice–ice collisions, and shattering of freezing droplets) also do not replicate observed HIWCs, with the results of the simulation including shattering of freezing droplets most closely resembling the observations. The simulation including all three SIP processes successfully produces HIWC regions at all temperature levels remarkably consistent with the observations in terms of ice number concentrations and radar reflectivity, which is not replicated using the original P3 two-ice scheme. This simulation shows that primary ice production plays a key role in generating HIWC regions at t < 30 min at temperatures < −40 °C, shattering of freezing droplets dominates ice particle production in HIWC regions at temperatures > − 15 °C during the early stage of convection, and fragmentation during ice–ice collisions dominates at temperatures > −15 °C during the later stage of convection and at temperatures < −20 °C over the whole convection period. This study confirms the dominant role of SIP processes in the formation of numerous small crystals in HIWC regions.

Retrievals of ice microphysics using dual-wavelength polarimetric radar observations during stratiform precipitation events

Ice growth processes within clouds affect the type as well as the amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR), Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-section area between both instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass-size relationship. The combination of dual-wavelength ratio, radar reflectivity and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can obtain ice particle shape, size and mass which are in line with differential radar reflectivity, dual-wavelength ratio and radar reflectivity observations when a suitable mass-size relation is used and when ice hydrometeors are assumed to be represented by oblate ice spheroids. A furthermore finding was the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD could reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.

Tornado Formation and Intensity Prediction Using Polarimetric Radar Estimates of Updraft Area

A sample of 198 supercells are investigated to determine if a radar proxy for the area of the storm midlevel updraft may be a skillful predictor of imminent tornado formation and/or peak tornado intensity. A novel algorithm, a modified version of the Thunderstorm Risk Estimation from Nowcasting Development via Size Sorting (TRENDSS) algorithm is used to estimate the area of the enhanced differential radar reflectivity factor (ZDR) column in Weather Surveillance Radar – 1988 Doppler data; the ZDR column area is used as a proxy for the area of the midlevel updraft. The areas of ZDR columns are compared for 154 tornadic supercells and 44 non-tornadic supercells, including 30+ supercells with tornadoes rated EF1, EF2, and EF3; nine supercells with EF4+ tornadoes also are analyzed. It is found that (i) at the time of their peak 0-1 km azimuthal shear, non-tornadic supercells have consistently small (< 20 km2) ZDR column areas while tornadic cases exhibit much greater variability in areas, and (ii) at the time of tornadogenesis, EF3+ tornadic cases have larger ZDR column areas than tornadic cases rated EF1/2. In addition, all nine violent tornadoes sampled have ZDR column areas > 30 km2 at the time of tornadogenesis. However, only weak positive correlation is found between ZDR column area and both radar-estimated peak tornado intensity and maximum tornado path width. Planned future work focused on mechanisms linking updraft size and tornado formation and intensity is summarized and the use of the modified TRENDSS algorithm, which is immune to ZDR bias and thus ideal for real-time operational use, is emphasized.