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Andrew Brown
,
Andrew Dowdy
,
Todd P. Lane
, and
Stacey Hitchcock

Abstract

Regional understanding of severe surface winds produced by convective processes [severe convective winds (SCWs)] is important for decision-making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and nonsevere events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between event types suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types.

Significance Statement

The purpose of this study is to improve regional understanding of different types of severe wind events in Australia, specifically those associated with atmospheric convection. We did this by constructing a dataset of 413 severe convective wind events, using weather station and radar data within 20 regions around Australia. We then split those events into three different types, based on the environmental conditions that they occur within. We found that each event type tends to occur at different times of the year and in different regions, while also having different wind gust and lightning characteristics. In addition, the atmospheric conditions that are helpful for prediction of severe wind events differs between each type. These results are intended to be useful for prediction of severe wind events associated with convection and assessing their variability, characteristics, and impacts, in both weather forecasting and climate analysis.

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Lidia Cucurull

Abstract

A Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) follow-on constellation, COSMIC-2, was successfully launched into equatorial orbit on 24 June 2019. With an increased signal-to-noise ratio due to improved receivers and digital beam-steering antennas, COSMIC-2 is producing about 5000 high-quality radio occultation (RO) profiles daily over the tropics and subtropics. The initial evaluation of the impact of assimilating COSMIC-2 into NOAA’s Global Forecast System (GFS) showed mixed results, and adjustments to quality control procedures and observation error characteristics had to be made prior to the assimilation of this dataset in the operational configuration in May 2020. Additional changes in the GFS that followed this initial operational implementation resulted in a larger percentage of rejection (∼90%) of all RO observations, including COSMIC-2, in the mid- to lower troposphere. Since then, two software upgrades directly related to the assimilation of RO bending angle observations were developed. These improvements were aimed at optimizing the utilization of COSMIC-2 and other RO observations to improve global weather analyses and forecasts. The first upgrade was implemented operationally in September 2021 and the second one in November 2022. This study describes both RO software upgrades and evaluates the impact of COSMIC-2 with this most recently improved configuration. Specifically, we show that the assimilation of COSMIC-2 observations has a significant impact in improving temperature and winds in the tropics, though benefits also extend to the extratropical latitudes.

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Han Zhang
,
Wansuo Duan
, and
Yichi Zhang

Abstract

The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.

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Free access
Ling Liu
,
Avichal Mehra
,
Daryl Kleist
,
Guillaume Vernieres
,
Travis Sluka
,
Kriti Bhargava
,
Patrick Stegmann
,
Hyun-Sook Kim
,
Shastri Paturi
,
Jiangtao Xu
, and
Ilya Rivin

Abstract

Realistic ocean initial conditions are essential for coupled hurricane forecasts. This study focuses on the impact of assimilating high-resolution ocean observations for initialization of the Modular Ocean Model (MOM6) in a coupled configuration with the Hurricane Analysis and Forecast System (HAFS). Based on the Joint Effort for Data Assimilation Integration (JEDI) framework, numerical experiments were performed for the Hurricane Isaias (2020) case, a category-1 hurricane, with use of underwater glider datasets and satellite observations. Assimilation of ocean glider data together with satellite observations provides opportunity to further advance understanding of ocean conditions and air–sea interactions in coupled model initialization and hurricane forecast systems. This comprehensive data assimilation approach has led to a more accurate prediction of the salinity-induced barrier layer thickness that suppresses vertical mixing and sea surface temperature cooling during the storm. Increased barrier layer thickness enhances ocean enthalpy flux into the lower atmosphere and potentially increases tropical cyclone intensity. Assimilation of satellite observations demonstrates improvement in Hurricane Isaias’s intensity forecast. Assimilating glider observations with broad spatial and temporal coverage along Isaias’s track in addition to satellite observations further increase Isaias’s intensity forecast. Overall, this case study demonstrates the importance of assimilating comprehensive marine observations to a more robust ocean and hurricane forecast under a unified JEDI–HAFS hurricane forecast system.

Significance Statement

This is the first comprehensive study of marine observations’ impact on hurricane forecast using marine JEDI. This study found that assimilating satellite observations increases upper-ocean stratification during the prestorm period of Isaias. Assimilating preprocessed observations from six gliders increases salinity-induced upper ocean barrier layer thickness, which reduces sea surface temperature cooling and increases enthalpy flux during the storm. This mechanism eventually enhances hurricane intensity forecast. Overall, this study demonstrates a positive impact of assimilating comprehensive marine observations to a successful ocean and hurricane forecast under a unified JEDI–HAFS hurricane forecast system.

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Jacopo Alessandri
,
Nadia Pinardi
,
Ivan Federico
, and
Andrea Valentini

Abstract

We developed a storm surge ensemble prediction system (EPS) for lagoons and transitional environments. Lagoons are often threatened by storm surge events with consequent risks for human life and economic losses. The uncertainties connected with a classic deterministic forecast are many, thus, an ensemble forecast system is required to properly consider them and inform the end-user community accordingly. The technological resources now available allow us to investigate the possibility of operational ensemble forecasting systems that will become increasingly essential for coastal management. We show the advantages and limitations of an EPS applied to a lagoon, using a very high-resolution unstructured grid finite element model and 45 EPS members. For five recent storm surge events, the EPS generally improves the forecast skill on the third forecast day compared to just one deterministic forecast, while they are similar in the first two days. A weighting system is implemented to compute an improved ensemble mean. The uncertainties regarding sea level due to meteorological forcing, river runoff, initial boundaries, and lateral boundaries are evaluated for a special case in the northern Adriatic Sea, and the different forecasts are used to compose the EPS members. We conclude that the largest uncertainty is in the initial and lateral boundary fields at different time and space scales, including the tidal components.

Significance Statement

Storm surges are extreme sea level events that may threaten densely populated coastal areas. The purpose of this work is to improve the extreme sea level forecast for transitional areas with the understanding of what are the most important forcing generating uncertainties and find a technique to reach a reliable sea level forecast. This is achieved by implementing an ensemble prediction system running 45 members for each event considered. Results show that initial and lateral boundary conditions provide most of the uncertainty, including the tidal components. The weighting system applied to find the ensemble mean improves the forecast skill on the third forecast day while it is comparable with the deterministic forecast in the first two days.

Open access
Hongyi Xiao
,
Juan Li
,
Guiqing Liu
,
Liwen Wang
, and
Yihong Bai

Abstract

The assimilation of two surface-sensitive channels of the AMSU-A instruments on board the NOAA-15/NOAA-18/NOAA-19 and MetOp-A/MetOp-B satellites over land was achieved in the China Meteorological Administration Global Forecast System (CMA_GFS). The land surface emissivity was calculated by 1) the window channel retrieval method and 2) the Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM2). Quality controls for these satellite microwave observations over land were conducted. The predictors and regression coefficients used for oceanic satellite data were retained during the bias correction over land and found to perform well. Three batch experiments were implemented in CMA_GFS with 4D-Var: 1) assimilating only the default data, and adding the above data over land with land surface emissivity obtained from 2) TELSEM2 and 3) the window channel retrieval method. The results indicated that the window channel retrieval method can better reduce the departure between the observed and simulated brightness temperature. Over most land types, the positive impacts of this method exceed those of TELSEM2. Both TELSEM2 and the window channel retrieval method improve the humidity analysis near the ground, as well as the forecast capability globally, particularly in those regions where the land coverage is greater, such as in the Northern Hemisphere. The data utilization of the two surface-sensitive channels increase by 6% and 12%, respectively, and the additional data every 6 h can cover most land, where there was no surface-sensitive data assimilated before. This study marks the beginning of near-surface channel assimilation over land in CMA_GFS and represents a breakthrough in the assimilation of other surface-sensitive channels in other satellite instruments.

Significance Statement

Surface-sensitive microwave channels are difficult to assimilate in NWP due to the lack of both direct measurement and appropriate modeling for instantaneous land surface emissivity. This paper discusses a method that improves the surface emissivity estimates, which has allowed the utilization of surface-sensitive microwave channels in CMA_GFS. Those capabilities have resulted in better data utilization, improved forecasts of temperature, geopotential height, and winds in the Northern Hemisphere at 3–7 days, and represent an incremental and important improvement to CMA_GFS.

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Jorge L. García-Franco
,
Chia-Ying Lee
,
Suzana J. Camargo
,
Michael K. Tippett
,
Daehyun Kim
,
Andrea Molod
, and
Young-Kwon Lim

Abstract

This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project. The global distribution of precipitation in S2S models shows relevant biases in the multimodel mean ensemble that are characterized by wet biases in total precipitation and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of the total precipitation biases in basins such as the southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence. The S2S models simulate too few TCs in the Atlantic and western North Pacific and too many TCs in the Southern Hemisphere and eastern North Pacific. At the storm scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300–500 km). An analysis of the mean TCP for each TC at each grid point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total precipitation.

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Jeffrey D. Kepert

Abstract

Parametric models of tropical cyclone winds are widely used for risk assessment. Although tropical cyclones often present their worst wind risk to humanity during landfall, parametric models that represent land–sea differences are rare. This paper presents a parametric model with explicit representation of land–sea differences. Statistical models were developed over each surface of the frictional wind speed reduction from gradient level to 10 m, and of the surface inflow angle, based on about 1200 simulations with a three-dimensional dynamical boundary layer model. The wind profile of Willoughby et al. is used to represent the gradient flow, and a maximum likelihood scheme used to fit this profile to best track data. The mean RMS difference between the statistical and dynamical surface winds within 100 km of the storm center is 0.78 m s−1 and 4.26° over sea, and 1.04 m s−1 and 4.59° over land. During landfall, the use of a common gradient-level structure, but different surface roughnesses, provides dynamical consistency between the estimated winds over sea and land. A simple representation of internal boundary layers is applied near the coast. Analysis of the dynamical simulations revealed substantial consistency with observational studies of the tropical cyclone boundary layer, including that the azimuth of the surface wind maximum is on average 65° from the front of the storm, in the left-forward quadrant in the Southern Hemisphere. There was, however, substantial variability around this figure, with the maximum occurring in the opposite forward quadrant in storms that were intense, and/or had a relatively rapid decrease in wind speed outside of the radius of maximum winds.

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Joseph Knisely
and
Jonathan Poterjoy

Abstract

The Hurricane Analysis and Forecasting System (HAFS) is the next-generation, FV3-based tropical cyclone (TC) forecasting system. Unlike operational implementations of NOAA’s Hurricane Weather Research and Forecast (HWRF) modeling system, current data assimilation (DA) capabilities in HAFS permit the uninterrupted basin-wide assimilation of measurements. This feature of HAFS opens a variety of new research directions for TC prediction, including new strategies for DA algorithm development and self-contained probabilistic forecasting. The present research focuses more narrowly on new opportunities HAFS brings for optimizing the use of satellite measurements for TC prediction. While satellite radiometers provide a wealth of information for characterizing temperature, moisture, and wind in TC environments, the provided measurements are often biased and contain unknown cross-channel error correlations. For mature global modeling systems, these statistics are estimated from information gathered during DA, namely, innovations collected over large spatial and temporal training periods. The estimated statistics, however, are imperfect owing to unknown error sources such as model process error, which are difficult to separate from observation error. As such, bias and uncertainty specifications that rely on information from external models are suboptimal—as is the current strategy for HWRF. In this paper, it will be demonstrated that bias estimation for satellite radiance observations is particularly sensitive to common design choices, such as using a bias model trained from the Global Data Assimilation System instead of within the native modeling system. Implications of this finding for TC prediction are examined over a 6-week period from 2020, which included the development and intensification of nine tropical cyclones.

Significance Statement

Tropical cyclone–focused numerical weather prediction is difficult due to complex nonlinear physical processes and a lack of in situ observations over open ocean. Prediction systems rely heavily on satellite radiance measurements, which have high spatial–temporal resolution over the entire domain but require bias correction. Estimation of observation bias requires long training periods and large spatial domain coverage, which is typically not permitted outside of global models. However, bias specification is strongly model dependent, as bias correction methods cannot easily separate model and observation bias. In this study, we perform satellite radiance bias specification internally for an experimental version of the NOAA Hurricane Analysis and Forecast System and demonstrate major implications for tropical cyclone prediction.

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