John R. Mecikalski

Deep moist convective updraughts are associated with various severe weather hazards, including hail formation, heavy precipitation, strong surface winds and turbulence. Yet, the actually realised hazards critically depend not only on the updraught strength, but also on the specific evolution related to the associated organised convective storm. A low-dimensional model of the governing updraught dynamics would potentially benefit parametrisations and short-term forecasting. However, despite ongoing efforts no satisfying low-dimensional model has been proposed so far. In this talk, we present a novel approach to deep-convective updraught modelling, motivated by the cauliflower-like visual appearance of cumulus clouds in the atmosphere. This appearance readily suggests that cumulus convection is the consequence of the collective dynamics of a large number of similar bubble-like elementary entities. Our model considers a central parcel that evolves according to classical parcel theory but is dynamically coupled to an ensemble of surrounding parcels. All parcels are modelled as (nonlinear, non-harmonic) oscillators. In the limit of very many interacting parcels, we adopt an approach from statistical physics to reduce the multi-body problem to a low-dimensional representation of the central-parcel evolution in terms of a generalised Langevin equation. This model essentially describes deep-convection updraughts as a Brownian motion in a potential well representing buoyancy. In particular, coupling of the central parcel to the environment causes a damping and (stochastic) forcing. We show that the model qualitatively agrees with well-reported features of atmospheric deep convection. For the quantitative assessment of our model, we compare with updraught measurements reported in the literature and those estimated from cumulus cloud-top cooling rates in geostationary satellite imagery. In particular, we give an account on the difficulties involved in directly comparing analytical models with these observations. Overall, we show that our updraught model correctly reproduces the governing features observed in atmospheric deep convection by correcting classical parcel theory. These results are encouraging and may stimulate new modelling attempts in the future.

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (Z_DR) and specific differential phase (K_DP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed Z_DR and K_DP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a Z_DR hole to indicate the existence of graupel in the low troposphere.

In 2009, the University of Alabama-Huntsville configured their GOES satellited-based solar radiation product to include Puerto Rico, the US Virgin Islands (USVI), Dominican Republic, Haiti, Jamaica, and Cuba. The half-hourly and daily integrated data are available at 1 km resolution for Puerto Rico and the USVI and 2 km for Hispaniola, Jamaica, and Cuba. These data made it possible to implement estimates of satellite radiation-based evapotranspiration methods on all of the islands. The use of the solar radiation data in combination with estimates of other climate parameters facilitated the development of a water and energy balance algorithm for Puerto Rico. The purpose of this paper is to describe the theoretical background and technical approach for estimating the components of the daily water and energy balance. The operational water and energy balance model is the first of its kind in Puerto Rico. Model validation results are presented for reference and actual evapotranspiration, soil moisture, and streamflow. Mean errors for all analyses were less than 7%. The water and energy balance model results can benefit such diverse fields as agriculture, ecology, coastal water management, human health, renewable energy development, water resources, drought monitoring, and disaster and emergency management. This research represents a preliminary step in developing a suite of gridded hydro-climate products for the Caribbean Region.

Few studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s^–1, tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

A solar insolation satellite remote sensing product for Puerto Rico, the US Virgin Islands (USVI), Dominican Republic, Haiti, Jamaica, and Cuba became available in 2009 through a collaboration between the University of Puerto Rico-Mayagüez Campus and the University of Alabama in Huntsville. Solar insolation data are available at 1 km resolution for Puerto Rico and the USVI and 2 km resolution for the other islands, as derived from 500 m resolution GOES-16 visible imagery. The insolation data demonstrate the powerful utility of satellite-derived fields for water resource applications, specifically the routine production of potential and reference evapotranspiration. This chapter describes the theoretical background and technical approach for estimating components of the daily water and energy balance in Puerto Rico. Useful information can be obtained from the model, which benefits disaster and emergency management, agriculture, human health, ecology, coastal water management, and renewable energy development at the island scale.

Super Rapid Scan Operations for the Geostationary Operational Environmental Satellite (GOES) R series (SRSOR) using GOES-14 have made experimentation with 1-min time-step data possible prior to the launch of the new satellite. A mesoscale atmospheric motion vector (mAMV) program is utilized in SRSOR with a Barnes analysis to produce objectively analyzed flow fields at the cloud tops of deep convection. Two nonsupercell and four supercell storm cases are analyzed. Data from the SRSOR mAMV analysis are compared with both multi-Doppler analyses when available and idealized convection cases within the Weather Research and Forecasting (WRF) Model framework. It is found that using SRSOR data provides several additional trackable targets to produce mAMVs in rapidly “bubbling” regions at the deep convective cloud-top level not previously available at lower temporal resolutions (<1 min). Results also show that supercell storm cases produce long-lived maxima in SRSOR cloud-top divergence (CTD) and “couplet” signatures in cloud-top vorticity (CTV), which when compared with idealized WRF Model simulations appear to form as a result of environmental horizontal vorticity tilting. Nonsupercell convection in contrast produced weaker, short-lived CTD signatures and no “CTV couplet” signatures. These case study results suggest that with SRSOR data it might be possible to uniquely identify supercells using only mAMV-derived flow fields.

A study was conducted to gain insights into the use of geostationary satellite‐based indicators for characterizing and identifying growing cumulus clouds that evolve into severe weather producing convective storms. Eleven convective initiation (CI), 41 cloud top temperature–effective radius (T‐r_e), and 9 additional fields were formed for 340 growing cumulus clouds that were manually tracked for 2 h and checked for association with severe weather to 2–3 h into the future. The geostationary satellite data were at 5 min resolution from Meteosat‐8 on six convectively active days in 2010, 2012, and 2013. The study's goals were to determine which satellite fields are useful to forecasting severe storms and to form a simple model for predicting future storm intensity. The CI fields were applied on 3 × 3 pixel regions, and the T‐r_e fields were analyzed on 9 × 9 and 51 × 51 pixel domains (needed when forming T‐r_e vertical profiles). Of the 340 growing cumulus clouds examined, 34 were later associated with severe weather (using European Severe Weather Database reports), with the remaining being nonsevere storms. Using a multivariate analysis, transforming predictors into their empirical posterior probability, and maximizing the Peirce skill score, the best predictors were T₁₄₅₁ (51 × 51 pixel T, where r_e exceeds 14 µm), T_G9 (9 × 9 pixel glaciation T surrounding a growing cloud), and Re_BRTG51 (51 × 51 pixel r_e at the breakpoint T in the T‐r_e profile). Rapid cloud growth prior to severe storm formation leads to delayed particle growth, colder temperatures of the first 14 µm particles, and lower T_G values.

A study was undertaken to examine growing cumulus clouds using 1–min time resolution “Super Rapid Scan Operations for Geostationary Operational Environmental Satellite (GOES)–R” (SRSOR) imagery to diagnose in-cloud processes from cloud-top information. SRSOR data were collected using GOES–14 for events in 2012–2014. Use of 1–min resolution SRSOR observations of rapidly changing scenes provides far more insights into cloud processes as compared to when present-day 5– to 15–min time resolution GOES data are used. For mid-day times on five days, cloud-top temperatures were cataloged for 71 cumulus clouds as they grew to possess anvils and often overshooting cloud tops, which occurred over 33–152 min time periods. Characteristics of the SRSOR-observed updrafts were examined individually, on a per-day basis, and collectively, to reveal unique aspects of updraft behavior, strength and acceleration as related to the ambient stability profile and cloud-top glaciation. A conclusion is that the 1–min observ...