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Free access
Chong-Chi Tong
,
Ming Xue
,
Chengsi Liu
,
Jingyao Luo
, and
Youngsun Jung

Abstract

To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multi-scale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the GSI data assimilation framework coupled with the FV3 limited area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner: 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together while radar data are assimilated in the second step.

The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 hours. More accurate hourly precipitation forecasts with MEnKF can last up to 24 hours for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events.

Restricted access
Xiangzhou Song
,
Xuehan Xie
,
Yunwei Yan
, and
Shang-Ping Xie

Abstract

Based on data collected from 14 buoys in the Gulf Stream, this study examines how hourly air–sea turbulent heat fluxes vary on subdaily time scales under different boundary layer stability conditions. The annual mean magnitudes of the subdaily variations in latent and sensible heat fluxes at all stations are 40 and 15 W m−2, respectively. Under near-neutral conditions, hourly fluctuations in air–sea humidity and temperature differences are the major drivers of subdaily variations in latent and sensible heat fluxes, respectively. When the boundary layer is stable, on the other hand, wind anomalies play a dominant role in shaping the subdaily variations in latent and sensible heat fluxes. In the context of a convectively unstable boundary layer, wind anomalies exert a strong controlling influence on subdaily variations in latent heat fluxes, whereas subdaily variations in sensible heat fluxes are equally determined by air–sea temperature difference and wind anomalies. The relative contributions by all physical quantities that affect subdaily variations in turbulent heat fluxes are further documented. For near-neutral and unstable boundary layers, the subdaily contributions are O(2) and O(1) W m−2 for latent and sensible heat fluxes, respectively, and they are less than O(1) W m−2 for turbulent heat fluxes under stable conditions.

Significance Statement

High-resolution buoy observations of air–sea variables in the Gulf Stream provide the opportunity to investigate the physical factors that determine subdaily variations in air–sea turbulent heat fluxes. This study addresses two key points. First, the observed subdaily amplitudes of heat fluxes are related to various processes, including wind fields and air–sea thermal effect differences. Second, the global sea surface heat budget is known to not be in near-zero balance and it ranges from several to tens of watts per square meter. Therefore, consideration of the relatively strong influence of subdaily variability in air–sea turbulent heat fluxes could provide a new strategy for solving the global heat budget balance problem.

Open access
Jeffrey Anderson
,
Chris Riedel
,
Molly Wieringa
,
Fairuz Ishraque
,
Marlee Smith
, and
Helen Kershaw

Abstract

The uncertainty associated with many observed and modeled quantities of interest in Earth system prediction can be represented by mixed probability distributions that are neither discrete nor continuous. For instance, a forecast probability of precipitation can have a finite probability of zero precipitation, consistent with a discrete distribution. However, nonzero values are not discrete and are represented by a continuous distribution; the same is true for rainfall rate. Other examples include snow depth, sea ice concentration, amount of a tracer or the source rate of a tracer. Some Earth system model parameters may also have discrete or mixed distributions. Most ensemble data assimilation methods do not explicitly consider the possibility of mixed distributions. The Quantile Conserving Ensemble Filtering Framework (Anderson 2022, 2023) is extended to explicitly deal with discrete or mixed distributions. An example is given using bounded normal rank histogram probability distributions applied to observing system simulation experiments in a low-order tracer advection model. Analyses of tracer concentration and tracer source are shown to be improved when using the extended methods. A key feature of the resulting ensembles is that there can be ensemble members with duplicate values. An extension of the rank histogram diagnostic method to deal with potential duplicates shows that the ensemble distributions from the extended assimilation methods are more consistent with the truth.

Restricted access
Matthias Zech
and
Lueder von Bremen

Abstract

Dynamical numerical weather prediction has remarkably improved over the last decades. Yet, postprocessing techniques are needed to calibrate forecasts which are based on statistical and Machine Learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on LASSO regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching Mean Squared Error Skill improvements of up to 3% (day-ahead) or 1% respectively (week ahead). Only considering land surface improvements in Europe, improvements of 4-6% for day-ahead and 1 to 5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

Restricted access
Javier Amezcua
,
Sven Peter Näsholm
, and
Ismael Vera-Rodriguez

Abstract

Infrasound waves generated at the Earth’s surface can reach high altitudes before returning to the surface to be recorded by microbarometer array stations. These waves carry information about the propagation medium, in particular, temperature and winds in the atmosphere. It is only recently that studies on the assimilation of such data into atmospheric models have been published. Intending to advance this line of research, we here use the Modulated Ensemble Transform Kalman Filter (METKF) –commonly used in satellite data assimilation– to assimilate infrasound-related observations in order to update a column of three vertically varying variables: temperature and horizontal wind speeds. This includes stratospheric and mesospheric heights, which are otherwise poorly observed. The numerical experiments on synthetic data but with realistic reanalysis product atmospheric specifications (following the Observing System Simulation Experiment paradigm) reveal that a large ensemble is capable of reducing errors, especially for the wind speeds in stratospheric heights close to 30 – 60 km. While using a small ensemble leads to incorrect analysis increments and large estimation errors, the METKF ameliorates this problem and even achieves error reduction from the prior to the posterior mean estimator.

Open access
Theresa Diefenbach
,
Leonhard Scheck
,
Martin Weissmann
, and
George Craig

Abstract

The analyses produced by a data assimilation system may be unbalanced, that is dynamically inconsistent with the forecasting model, leading to noisy forecasts and reduced skill. While there are effective procedures to reduce synoptic-scale imbalance, the situation on the convective scale is less clear because the flowon this scale is strongly divergent and non-hydrostatic. In this studywe compare three measures of imbalance relevant to convective-scale data assimilation: (i) surface pressure tendencies, (ii) vertical velocity variance in the vicinity of convective clouds, and (iii) departures from the vertical velocity prescribed by the weak temperature gradient (WTG) approximation. These are applied in a numerical weather prediction system, with three different data assimilation algorithms: 1. Latent Heat Nudging (LHN), 2. Local Ensemble Transform Kalman Filter (LETKF), and 3. LETKF in combination with incremental analysis updates (IAU). Results indicate that surface pressure tendency diagnoses a different type of imbalance than the vertical velocity variance and theWTG departure. The LETKF induces a spike in surface pressure tendencies, with a large-scale spatial pattern that is not clearly related to the precipitation pattern. This anomaly is notably reduced by the IAU. LHN does not generate a pronounced signal in the surface pressure, but produces the most imbalance in the other two measures. The imbalances measured by the partitioned vertical velocity variance andWTG departures are similar, and closely coupled to the convective precipitation. Between these two measures, the WTG departure has the advantage of being simpler and more economical to compute.

Restricted access
Connell S. Miller
,
Gregory A. Kopp
,
David M.L. Sills
, and
Daniel G. Butt

Abstract

Currently, the Enhanced Fujita scale does not consider the wind-induced movement of various large compact objects such as vehicles, construction equipment, farming equipment / haybales, etc. that are often found in post-event damage surveys. One reason for this is that modelling debris in tornadoes comes with considerable uncertainties since there are many parameters to determine, leading to difficulties in using trajectories to analyze wind speeds of tornadoes. This paper aims to develop a forensic tool using analytical tornado models to estimate lofting wind speeds based on trajectories of large compact objects. This is accomplished by implementing a Monte Carlo simulation to randomly select the parameters and plotting cumulative distribution functions showing the likelihood of lofting at each wind speed. After analyzing the debris lofting from several documented tornadoes in Canada, the results indicate that the method provides threshold lofting wind speeds that are similar to the estimated speeds given by other methods. However, the introduction of trajectories produces estimated lofting wind speeds that are higher than the EF-scale rating given from the ground survey assessment based on structural damage. Further studies will be required to better understand these differences.

Restricted access
Joël Stein
and
Fabien Stoop

Abstract

A procedure for evaluating the quality of probabilistic forecasts of binary events has been developed. This is based on a two-step procedure: pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood in order to obtain frequencies at the neighborhood length scale and then to calculate the Brier divergence for these neighborhood frequencies. This score allows the comparison of a probabilistic forecast and observations at the neighborhood length scale, and therefore, the rewarding of event forecasts shifted from the location of the observed event by a distance smaller than the neighborhood size. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. The neighborhood Brier divergence skill score (BDnSS) measures the performance of the probabilistic forecast against the sample climatology. BDnSS and its decomposition have been used for idealized and real cases in order to show the utility of neighborhoods when comparing at different scales the performances of ensemble forecasts between themselves or with deterministic forecasts or of deterministic forecasts between themselves.

Significance Statement

A pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood, is performed in order to obtain frequencies at the neighborhood scale. The Brier divergence is then calculated for these neighborhood frequencies to compare a probabilistic forecast and observations at the neighborhood scale. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. This uncertainty term is used to define the neighborhood Brier divergence skill score which is an alternative to the popular fractions skill score, with a more appropriate denominator.

Open access
Brian H. Tang
,
Rosimar Rios-Berrios
, and
Jun A. Zhang

Abstract

This study presents a method to diagnose radial ventilation, the horizontal flux of relatively low-θ e air into tropical cyclones, from dropsonde observations. We used this method to investigate ventilation changes over three consecutive sampling periods in Hurricane Sam (2021), which underwent substantial intensity changes over three days. During the first and last periods, coinciding with intensification, the ventilation was relatively small due to a lack of spatial correlation between radial flow and θ e azimuthal asymmetries. During the second period, coinciding with weakening, the ventilation was relatively large. The increased ventilation was caused by greater shear associated with an upper-level trough, tilting the vortex, along with dry, low-θ e air wrapping in upshear. The spatial correlation of the radial inflow and anomalously low-θ e air resulted in large ventilation at mid-to-upper levels. Additionally, at low-to-mid levels, there was evidence of mesoscale inflow of low-θ e air in the stationary band complex. The location of these radial ventilation pathways and their effects on Sam’s intensity are consistent with previous idealized and real-case modeling studies. More generally, this method offers a way to monitor ventilation changes in tropical cyclones, particularly when there is full-troposphere sampling around and within a tropical cyclone’s core.

Restricted access