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Peter Bechtold
,
Jean Pierre Pinty
, and
Patrick Mascart

Abstract

A method is proposed on how to handle the effects of partial cloudiness in a warm-rain microphysical scheme and how to generate subgrid-scale precipitation. The method is simple and concerns essentially two ideas: the use of the vertical distribution of the partial cloudiness and the use of environmental and cloud-scale values for the thermodynamic variables instead of their grid-mean values. It applies to any microphysical scheme.

Here, the method has been applied to a warm-rain parameterization scheme that has been implemented in a mesoscale model using a statistical partial cloudiness scheme. Numerical tests have been done for two one-dimensional cases of boundary-layer cloudiness: a cumulus case and a case of a decoupled stratocumulus layer.

The results show that the correct coupling of a partial cloudiness scheme and a microphysical scheme allows for a better description of the actual cloudiness and precipitation fields by ensuring a consistent computation of partial cloudiness, cloud water, and rainwater in partly cloudy regions.

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Jun-Ichi Yano
,
Peter Bechtold
,
Jean-Luc Redelsperger
, and
Francoise Guichard

Abstract

The capacity of wavelets to effectively represent atmospheric processes under compression is tested by a dataset generated by a cloud-resolving model simulation of deep convective events observed during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE).

Generally, more than 90% of the total variance is reproduced by retaining only the top 10% of the modes. The compressed data does not drastically deteriorate for graphic purposes, even when only the top 1% of modes are retained. The performance of compression is overall comparable for all the wavelets considered and also does not strongly depend on the type of physical variables.

Conventional quantitative measures do not distinguish the compression errors arising from different characters of individual wavelets well, although different wavelet modes filter out different structures as “noises” depending on their characteristics. The importance of choosing wavelets that represent the shape of signals physically expected is emphasized. Analytical discontinuities of wavelets are not necessarily undesirable for all the purposes, but must be consistent with our physical picture for the system. For this reason, the Haar wavelet may be acceptable because of its piecewise-constant structure, whereas the Daubechies wavelets of lower degrees are less appropriate because of their highly irregular structures.

Some preliminary analyses are performed for assessing the capacity of wavelets to represent the full physics of meteorological systems. It is suggested that the loss of magnitudes in vertical fluxes under high compression can be recovered by a kind of “renormalization” to a good extent. The mass continuity is found to be reasonably satisfied under the proposed compression method, although the latter is not explicitly constrained by the former.

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Zied Ben Bouallègue
,
Fenwick Cooper
,
Matthew Chantry
,
Peter Düben
,
Peter Bechtold
, and
Irina Sandu

Abstract

Based on the principle “learn from past errors to correct current forecasts,” statistical postprocessing consists of optimizing forecasts generated by numerical weather prediction (NWP) models. In this context, machine learning (ML) offers state-of-the-art tools for training statistical models and making predictions based on large datasets. In our study, ML-based solutions are developed to reduce forecast errors of 2-m temperature and 10-m wind speed of the ECMWF’s operational medium-range, high-resolution forecasts produced with the Integrated Forecasting System (IFS). IFS forecasts and other spatiotemporal indicators are used as predictors after careful selection with the help of ML interpretability tools. Different ML approaches are tested: linear regression, random forest decision trees, and neural networks. Statistical models of systematic and random errors are derived sequentially where the random error is defined as the residual error after bias correction. In terms of output, bias correction and forecast uncertainty prediction are made available at any point from locations around the world. All three ML methods show a similar ability to capture situation-dependent biases leading to noteworthy performance improvements (between 10% and 15% improvement in terms of root-mean-square error for all lead times and variables), and a similar ability to provide reliable uncertainty predictions.

Open access
Jian Ling
,
Peter Bauer
,
Peter Bechtold
,
Anton Beljaars
,
Richard Forbes
,
Frederic Vitart
,
Marcela Ulate
, and
Chidong Zhang

Abstract

This study introduces a concept of global versus local forecast skill of the Madden–Julian oscillation (MJO). The global skill, measured by a commonly used MJO index [the Real-time Multivariate MJO (RMM)], evaluates the model’s capability of forecasting global patterns of the MJO, with an emphasis on the zonal wind fields. The local skill is measured by a method of tracking the eastward propagation of MJO precipitation. It provides quantitative information of the strength, propagation speed, and timing of MJO precipitation in a given region, such as the Indian Ocean. Both global and local MJO forecast skills are assessed for ECMWF forecasts of three MJO events during the 2011–12 Dynamics of the MJO (DYNAMO) field campaign. Characteristics of error growth differ substantially between global and local MJO forecast skills, and between the three MJO quantities (strength, speed, and timing) of the local skill measure. They all vary considerably among the three MJO events. Deterioration in global forecast skill for these three events appears to be related to poor local skill in forecasting the propagation speed of MJO precipitation. The global and local MJO forecast skill measures are also applied to evaluate numerical experiments of observation denial, humidity relaxation, and forcing by daily perturbations in sea surface temperature (SST). The results suggest that forecast skill or errors of convective initiation of the three MJO events have global origins. Effects of local (Indian Ocean) factors, such as enhanced observations in the initial conditions, variability of tropospheric humidity and tropical SST, on forecasts of MJO initiation and propagation are limited.

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Sharanya J. Majumdar
,
Linus Magnusson
,
Peter Bechtold
,
Jean Raymond Bidlot
, and
James D. Doyle

Abstract

Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (EC4) with upgraded moist physics is compared with a 9-km version (EC9) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS–Tropical Cyclones (COAMPS-TC) system (CO4) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4, resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum winds (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36-h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure–wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex for TCs with initial intensity ≥55 kt (∼28 m s−1) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid–upper-tropospheric heating.

Significance Statement

Improved forecasts of tropical storms and hurricanes depend on advances in computer weather models. We tested an experimental high-resolution (4 km) version of the global ECMWF model against its 9-km counterpart to evaluate the influence of resolution on storm position and intensity. We also compared this with the 4-km U.S. Navy model, which is designed for tropical storms and hurricanes. Over a 3-month period during the active 2020 Atlantic hurricane season, we found that increasing the horizontal resolution improved intensity forecasts. The Navy model forecasts were superior for the radius of maximum winds and had lower intensity biases. Two additional experiments with the ECMWF model revealed the importance of simulating air–sea interaction in high winds and current challenges with explicitly simulating deep thunderstorm clouds in their system.

Free access
Juliana Dias
,
Maria Gehne
,
George N. Kiladis
,
Naoko Sakaeda
,
Peter Bechtold
, and
Thomas Haiden

Abstract

Despite decades of research on the role of moist convective processes in large-scale tropical dynamics, tropical forecast skill in operational models is still deficient when compared to the extratropics, even at short lead times. Here we compare tropical and Northern Hemisphere (NH) forecast skill for quantitative precipitation forecasts (QPFs) in the NCEP Global Forecast System (GFS) and ECMWF Integrated Forecast System (IFS) during January 2015–March 2016. Results reveal that, in general, initial conditions are reasonably well estimated in both forecast systems, as indicated by relatively good skill scores for the 6–24-h forecasts. However, overall, tropical QPF forecasts in both systems are not considered useful by typical metrics much beyond 4 days. To quantify the relationship between QPF and dynamical skill, space–time spectra and coherence of rainfall and divergence fields are calculated. It is shown that while tropical variability is too weak in both models, the IFS is more skillful in propagating tropical waves for longer lead times. In agreement with past studies demonstrating that extratropical skill is partially drawn from the tropics, a comparison of daily skill in the tropics versus NH suggests that in both models NH forecast skill at lead times beyond day 3 is enhanced by tropical skill in the first couple of days. As shown in previous work, this study indicates that the differences in physics used in each system, in particular, how moist convective processes are coupled to the large-scale flow through these parameterizations, appear as a major source of tropical forecast errors.

Full access
Lisa Bengtsson
,
Juliana Dias
,
Maria Gehne
,
Peter Bechtold
,
Jeffrey Whitaker
,
Jian-Wen Bao
,
Linus Magnusson
,
Sara Michelson
,
Philip Pegion
,
Stefan Tulich
, and
George N. Kiladis

Abstract

There is a longstanding challenge in numerical weather and climate prediction to accurately model tropical wave variability, including convectively coupled equatorial waves (CCEWs) and the Madden–Julian oscillation. For subseasonal prediction, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) has been shown to be superior to the NOAA Global Forecast System (GFS) in simulating tropical variability, suggesting that the ECMWF model is better at simulating the interaction between cumulus convection and the large-scale tropical circulation. In this study, we experiment with the cumulus convection scheme of the ECMWF IFS in a research version of the GFS to understand which aspects of the IFS cumulus convection scheme outperform those of the GFS convection scheme in the tropics. We show that the IFS cumulus convection scheme produces significantly different tropical moisture and temperature tendency profiles from those simulated by the GFS convection scheme when it is coupled with other physics schemes in the GFS physics package. We show that a consistent treatment of the interaction between parameterized convective plumes in the GFS planetary boundary layer (PBL) and the IFS convection scheme is required for the GFS to replicate the tropical temperature and moisture profiles simulated by the IFS model. The GFS model with the IFS convection scheme, and the consistent treatment between the convection and PBL schemes, produces much more organized convection in the tropics, and generates tropical waves that propagate more coherently than the GFS in its default configuration due to better simulated interaction between low-level convergence and precipitation.

Free access