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Jana Lesak Houser, Howard B. Bluestein, Kyle Thiem, Jeffrey Snyder, Dylan Reif, and Zachary Wienhoff

Abstract

This study builds upon recent rapid-scan radar observations of mesocyclonic tornadogenesis in supercells by investigating the formation of seven tornadoes (four from a single cyclic supercell), most of which include samples at heights < 100 m above radar level. The spatio-temporal evolution of the tornadic vortex signatures (TVSs), maximum velocity differentials across the vortex couplet, and pseudovorticity are analyzed. In general, the tornadoes formed following a non-descending pattern of evolution, although one case was descending over time scales O(<60 s) and the evolution of another case was dependent upon the criteria used to define a tornado, and may have been associated with a rapidly occurring top-down process. Thus, it was determined that the vertical sense of evolution of a tornado can be sensitive to the criteria employed to define a TVS. Furthermore, multiple instances were found in which TVSs terminated at heights below 1.5 km, although vertical sampling above this height was often limited.

Open access
Phuong-Nghi Do, Kao-Shen Chung, Pay-Liam Lin, Ching-Yin Ke, and Scott M. Ellis

Abstract

This study investigated the effect of the assimilation of the S- and Ka-band dual‐wavelength-retrieved water vapor data with radial wind and reflectivity data. The vertical profile of humidity, which provides environmental information before precipitation occurs, was obtained at low levels and thinned into averaged and four-quadrant profiles. Additionally, the following two strategies were examined: 1) assimilation of water vapor data with radar data for the entire 2 h and 2) assimilation of water vapor data in the first hour, and radial velocity and reflectivity data in the second hour. By using the WRF local ensemble transform Kalman filter data assimilation system, three real cases of the Dynamics of the Madden–Julian Oscillation experiment were examined through a series of experiments. The analysis results revealed that assimilating additional water vapor data more markedly improved the analysis at the convective scale than assimilating radial wind and reflectivity data alone. In addition, the strategy of assimilating only retrieved water vapor data in the first hour and radial wind and reflectivity data in the second hour achieved the optimal analysis and subsequent very short-term forecast. The evaluation of quantitative precipitation forecasting demonstrated that assimilating additional retrieved water vapor data distinctly improved the rain forecast compared with assimilating radar data only. When moisture data were assimilated, improved nowcasting could be extended up to 4 h. Furthermore, assimilating moisture profiles into four quadrants achieved more accurate analysis and forecast. Overall, our study demonstrated that the humidify information in nonprecipitation areas is critical for further improving the analysis and forecast of convective weather systems.

Open access
David I. Duncan, Niels Bormann, Alan J. Geer, and Peter Weston

Abstract

Radiances from microwave temperature sounders have been assimilated operationally at ECMWF for two decades, but observations significantly affected by clouds and precipitation have been screened out. Extending successful assimilation beyond clear-sky scenes is a challenge that has taken several years of development to achieve. In this paper we describe the all-sky treatment of AMSU-A, which enables greater numbers of temperature sounding radiances to be used in meteorologically active parts of the troposphere. Successful all-sky assimilation required combining lessons learned from the clear-sky assimilation of AMSU-A with the approach initially developed for humidity-sensitive microwave radiances. This concerned particularly observation thinning, error modeling, and variational quality control. As a result of the move to all-sky assimilation, the forecast impact of AMSU-A now replicates and exceeds that of the previous clear-sky usage. This is shown via trials in comparison to the current ECMWF assimilation system, judged with respect to forecast scores and background fits to independent observations. Persistently cloudy regions and phenomena such as tropical cyclones are better sampled when assimilating AMSU-A in all-sky conditions, causing an increase of about 13% in used channel-5 radiances globally. These impacts are explored, with an emphasis on tropical cyclones in the 2019 season. Independent observations provide consistent evidence that representation of humidity is improved, for example, while extratropical Z500 forecasts are improved by about 0.5% out to at least day 2. On the strength of these results, assimilation of AMSU-A moved to all-sky conditions with the upgrade to IFS cycle 47R3 in October 2021.

Open access
Mathieu Lachapelle and Julie M. Thériault

Abstract

Freezing rain and ice pellets are particularly difficult to forecast when solid precipitation is completely melted aloft. This study addresses this issue by investigating the processes that led to a long-duration ice pellet event in Montreal, Québec, Canada, on 11–12 January 2020. To do so, a benchmark model initialized with ERA5 data is used to show that solid precipitation was completely melted below the melting layer, which discards partial melting from the possible ice pellet formation processes. Macro photography of precipitation reveals that small columnar crystals (∼200 μm) and ice pellets occurred simultaneously for more than 10 h. The estimation of ice crystal number concentration using macro photographs and laser-optical disdrometer data suggests that all supercooled drops could have refrozen by contact freezing with ice crystals. Rimed ice pellets also indicate ice supersaturation in the subfreezing layer. Given these observations, the formation of ice pellets and ice crystals was probably promoted by secondary ice production and the horizontal advection of ice crystals below the melting layer, as we illustrate using a conceptual model. Overall, these findings demonstrate how ice nucleation processes at temperatures near 0°C can drastically change the precipitation phase and the impact of a storm.

Significance Statement

Ice pellets are generally formed when snow particles partially melt while falling through a warm layer aloft before completely refreezing in a cold layer closer to the surface. Ice pellets can also be formed when snow particles completely melt aloft, but freezing rain is often produced in such conditions. On 11–12 January 2020, ice pellets were produced during more than 10 h in Montreal, Quebec, Canada. Macro photographs of the precipitation particles show that ice pellets occurred simultaneously with small ice crystals. Most of the ice pellets were produced while snow particles were completely melted aloft. The supercooled drops probably refroze due to collisions with the ice crystals that could have been advected by the northeasterly winds near the surface.

Open access
Roland Potthast, Klaus Vobig, Ulrich Blahak, and Clemens Simmer

Abstract

We investigate the assimilation of nowcasted information into a classical data assimilation cycle. As a reference setup, we employ the assimilation of standard observations such as direct observations of particular variables into a forecasting system. The pure advective movement extrapolation of observations as a simple nowcasting (NWC) is usually much better for the first minutes to hours, until outperformed by numerical weather prediction (NWP) based on data assimilation. Can nowcasted information be used in the data assimilation cycle? We study both an oscillator model and the Lorenz 63 model with assimilation based on the localized ensemble transform Kalman filter (LETKF). We investigate and provide a mathematical framework for the assimilation of nowcasted information, approximated as a local tendency, into the LETKF in each assimilation step. In particular, we derive and discuss adequate observation error and background uncertainty covariance matrices and interpret the assimilation of nowcasted information as assimilation with an H 1-type metric in observation space. Further, we show numerical results that prove that nowcasted information in data assimilation has the potential to significantly improve model based forecasting.

Open access
Zhe Feng, Adam Varble, Joseph Hardin, James Marquis, Alexis Hunzinger, Zhixiao Zhang, and Mandana Thieman

Abstract

This study characterizes the wide range of deep convective cloud life cycles and their relationships with ambient environments observed during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign near the Sierras de Córdoba (SDC) range in central Argentina. We develop a novel convective cell tracking database for the entire field campaign using C-band polarimetric radar observations. The cell tracking database includes timing, location, area, depth, merge/split information, microphysical properties, collocated satellite-retrieved cloud properties, and sounding-derived environmental conditions. Results show that the SDC exerts a strong control on convection initiation (CI) and growth. CI preferentially occurs east of the SDC ridge during the afternoon, and cells often undergo upscale growth through the evening as they travel eastward toward the plains. Larger and more intense cells tend to occur in more unstable and humid low-level environments, and surface-based cells are stronger than elevated cells. Midtropospheric relative humidity and vertical wind shear also jointly affect the size and depth of the cells. Rapid cell area growth rates exhibit dependence on both large environmental wind shear and low-level moisture. Evolution of convective cell macro- and microphysical properties are strongly influenced by convective available potential energy and low-level humidity, as well as the presence of other cells in their vicinity. This cell tracking database demonstrates a framework that ties measurements from various platforms centering around convective life cycles to facilitate process understanding of factors that control convective evolution.

Significance Statement

The purpose of this study is to develop a framework that ties coordinated radar, satellite, and radiosonde measurements around tracking convective storm life cycles to facilitate process understanding of atmospheric environments that control storm evolution. The processes coupling storm life cycles and local environments remain inadequately understood and are poorly represented in weather and climate models. Our results demonstrate the importance of atmospheric instability, low- and midtropospheric moisture, changes of wind with height, and interactions among nearby storms in affecting the formation and growth of convective storms. The storm database developed in this work enables future studies for comprehensive exploration of processes that lead to improved mechanistic understanding of storm evolution and their representations in models.

Open access
Masih Eghdami, Ana P. Barros, Pedro A. Jiménez, Timothy W. Juliano, and Branko Kosovic

Abstract

Accurate representation of heterogeneous surface layer processes is essential for numerical weather prediction (NWP) with sub-kilometer grid spacing. NWP models such as the Weather Research and Forecasting (WRF) Model generally use second-moment turbulent models for parameterizing the planetary boundary layer (PBL). The most common parameterizations follow Mellor–Yamada and account for the vertical turbulent mixing only; that is, standard PBL parameterizations are one-dimensional (1DPBL). The horizontal diffusion of momentum is parameterized based on Smagorinsky’s model for numerical stability. Although the combination of 1DPBL and 2D Smagorinsky parameterizations is successful at coarse grid resolutions (e.g., grid-size dx ∼ 12–2 km), it does not represent well the effect of horizontal turbulence as gridcell size decreases (<1 km). To reconcile the representation of vertical and horizontal turbulent mixing, a full three-dimensional PBL scheme (3DPBL) based on the Mellor–Yamada model was implemented in WRF. The 3DPBL uses the horizontal and vertical turbulent fluxes diagnosed from the flow gradients to handle the turbulent mixing. These gradients cannot be directly calculated near the surface. Therefore, the 3DPBL parameterization is coupled herein to a second-order diagnostic model of the three-dimensional turbulent fluxes in the surface layer. Several adjustments to the original Mellor–Yamada model, including a modified length scale, were introduced to capture flow anisotropy and dependence on stability conditions. The results are compared against data from the Wind Forecast Improvement Project 2 (WFIP2) for different weather regimes and using different grid resolutions to examine stability and scale dependency.

Open access
Chiem van Straaten, Kirien Whan, Dim Coumou, Bart van den Hurk, and Maurice Schmeits

Abstract

Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

Open access
Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

An ensemble precipitation forecast post-processing method is proposed by hybridizing the Analog Ensemble (AnEn), Minimum Divergence Schaake Shuffle (MDSS), and Convolutional Neural Network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7-days. The AnEn-CNN hybrid post-processing is trained on the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in Continuous Ranked Probability Skill Score. Further, it outperforms other AnEn methods by 0-60% in terms of Brier Skill Score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical post-processing and neural networks, and is one of only a few studies pertaining to precipitation ensemble post-processing in BC.

Open access
David M. Schultz and Peter Lynch
Open access