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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.

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Martin Schön
,
Vasileios Savvakis
,
Maria Kezoudi
,
Andreas Platis
, and
Jens Bange

Abstract

Atmospheric aerosols affect human health and influence atmospheric and biological processes. Dust can be transported long distances in the atmosphere, and the mechanisms that influence dust transport are not fully understood. To improve the database for numerical models that simulate dust transport, measurements are needed that cover both the vertical distribution of the dust and its size distribution. In addition to measurements with crewed aircraft, uncrewed aircraft systems (UASs) provide a particularly suitable platform for this purpose. In this paper, we present a payload for the small fixed-wing UAS of the type Multiple-Purpose Airborne Sensor Carrier 3 (MASC-3) for aerosol particle measurements that is based on the optical particle counter (OPC) OPC-N3 (Alphasense, United Kingdom), modified by the addition of a dryer and a passive aspiration system (OPC-Pod). Based on field tests with a reference instrument in Mannheim, Germany, wind tunnel tests, and a comparison measurement with the UAS-mounted aerosol particle measurement Universal Cloud and Aerosol Sounding System (UCASS) during a dust event over Cyprus, we show that the OPC-Pod can measure particle number concentrations in the range of 0.66–31 μm as well as particle size distributions. The agreement of the OPC-Pod with UCASS is good. Both instruments resolve a vertical profile of the Saharan dust event, with a prominent dust layer between 1500 and 2800 m MSL, with particle number concentrations up to 35 cm−3 for particles between 0.66 and 31 μm.

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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.

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Gregory J. Hakim
and
Sanjit Masanam

Abstract

Global deep-learning weather prediction models have recently been shown to produce forecasts that rival those from physics-based models run at operational centers. It is unclear whether these models have encoded atmospheric dynamics, or simply pattern matching that produces the smallest forecast error. Answering this question is crucial to establishing the utility of these models as tools for basic science. Here we subject one such model, Pangu-Weather, to a set of four classical dynamical experiments that do not resemble the model training data. Localized perturbations to the model output and the initial conditions are added to steady time-averaged conditions, to assess the propagation speed and structural evolution of signals away from the local source. Perturbing the model physics by adding a steady tropical heat source results in a classical Matsuno–Gill response near the heating, and planetary waves that radiate into the extratropics. A localized disturbance on the winter-averaged North Pacific jet stream produces realistic extratropical cyclones and fronts, including the spontaneous emergence of polar lows. Perturbing the 500hPa height field alone yields adjustment from a state of rest to one of wind–pressure balance over ∼6 hours. Localized subtropical low pressure systems produce Atlantic hurricanes, provided the initial amplitude exceeds about 4 hPa, and setting the initial humidity to zero eliminates hurricane development. We conclude that the model encodes realistic physics in all experiments, and suggest it can be used as a tool for rapidly testing a wide range of hypotheses.

Open 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.

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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
Nicolas G. Alonso-De-Linaje
,
Andrea N. Hahmann
,
Ioanna Karagali
,
Krystallia Dimitriadou
, and
Merete Badger

Abstract

The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind Lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the one-year-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples.

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Tyler Cox
,
Aaron Donohoe
,
Kyle C. Armour
,
Gerard H. Roe
, and
Dargan M.W. Frierson

Abstract

Atmospheric heat transport (AHT) is an important piece of our climate system, but has primarily been studied at monthly or longer time scales. We introduce a new method for calculating zonal-mean meridional atmospheric heat transport (AHT) using instantaneous atmospheric fields. When time averaged, our calculations closely reproduce the climatological AHT used elsewhere in the literature to understand AHT and its trends on long timescales. In the extratropics, AHT convergence and atmospheric heating are strongly temporally correlated suggesting that AHT drives the vast majority of zonal-mean atmospheric temperature variability. Our AHT methodology separates AHT into two components, eddies and the mean-meridional circulation, which we find are negatively correlated throughout most of the mid- to high-latitudes. This negative correlation reduces the variance of total AHT compared to eddy AHT. Lastly, we find that the temporal distribution of total AHT at any given latitude is approximately symmetric.

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Yun Chang
and
Alberto Scotti

Abstract

This paper provides a framework that unifies the characteristics of Langmuir turbulence, including the vortex force effect, velocity scalings, vertical flow structure, and crosswind spacing between surface streaks. The widely accepted CL2 mechanism is extended to explain the observed maximum alongwind velocity and downwelling velocity below the surface. Balancing the extended mechanism in the Craik-Leibovich equations, the scalings for the along-wind velocity u, cross-wind velocity v, and vertical velocity w are formulated as
U = U f L a 2 / 3 , V = W = ( U f 2 U s ) 1 / 3 .
Here, Uf is the friction velocity, Us is the Stokes drift on the surface, and La = (Uf /Us )1/2 is the Langmuir number. Simulations using the Stratified Ocean Model with Adaptive Refinement in Large Eddy Simulation mode (LES-SOMAR) validate the scalings and reveal physical similarity for velocity and crosswind spacing. The horizontally averaged velocity along the wind ū/U on the surface grows with time, whereas v/V and w/W are confined. The root mean square (rms) of w peaks at wrms/W ≈ 0.85 at a depth of 1.3Zs, where Zs is the e-folding scale of the Stokes drift. The crosswind spacing L grows linearly with time but is finally limited by the depth of the water H, with maximum L/H = 3.3. This framework agrees with measurement collected in six different field campaigns.
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Fan Wu
and
Kelly Lombardo

Abstract

This study employs 3D idealized numerical experiments to investigate the physical processes associated with coastal convection initiation (CI) as an offshore-moving squall line traverses a mountainous coastal region. A squall line can propagate discretely as convection initiates over the lee slope downstream of the primary storm as the cold pool collides with a sea breeze. Intensity of the initiating convection, thus the downstream squall line, is sensitive to the sea breeze numerical initialization method, since it influences sea breeze and cold pool characteristics, instability and vertical wind shear in the sea breeze environment, and ultimately the vertical acceleration of air parcels during CI. Here, sea breezes are generated through four commonly used numerical methods: a cold-block marine atmospheric boundary layer (MABL), prescribed surface sensible heat flux function, prescribed surface sensible plus latent heat flux functions, and radiation plus surface-layer parameterization schemes. For MABL-initialized sea breezes, shallow weak sea breeze flow in a relatively low instability environment results in weak CI. For the remainder, deeper stronger sea breeze flow in an environment of enhanced instability supports more robust CI. In a subset of experiments, however, the vertical trajectory of air parcels is suppressed leading to weaker convection. Downward acceleration forms due to the horizontal rotation of the sea breeze flow. Accurate simulations of coastal convective storms rely on an accurate representation of sea breezes. For idealized experiments such as the present simulations, a combination of initialization methods likely produces a more realistic representation of sea breeze and the associated physical processes.

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