Ozonsimulationen mit ICON für die Verbesserung von UV-Vorhersagen

<p class="Default">Ozon (O<sub>3</sub>) in der Stratosphäre absorbiert die biologisch schädliche ultraviolette Strahlung der Sonne (den größten Teil der UV-B-Strahlung) und verhindert, dass sie die Erdoberfläche erreicht. Die energiereiche UV-Strahlung kann das genetische Material in den Zellen von Pflanzen und Tieren, sowie von Menschen zerstören. Ohne die stratosphärische Ozonschicht wäre das Leben auf der Erde, wie wir es kennen, nicht möglich.</p> <p class="Default">Der Deutsche Wetterdienst (DWD) stellt UV-Indexkarten zur Verfügung, um die Bevölkerung bezgl. hoher UV-Belastungen zu informieren und zu warnen [1]. Dazu werden Daten aus dem golobalen Vorhersagemodell ICON (ICOsahedral Non-hydrostatic model) [2], externe Ozondaten und ein eigenes UV-Modell verwendet, um eine Vorhersage des UV-Index zu erstellen, der z.B. auf der DWD-Webseite als Vorhersage visualisiert wird.</p> <p class="Default">In diesem Projekt wird in Zusammenarbeit mit dem DWD ein selbstkonsistentes System entwickelt, um UV-Indexkarten vollständig mittels ICON zu generieren. Zu diesem Zweck wird ein linearisiertes Ozonschema (LINOZ) [3] für tägliche Ozonvorhersagen optimiert. Dies geschieht als Erweiterung der ICON-ART Struktur [4] [5] (ART: Aerosols and Reactive Trace gases). Für die Berechnung von UV-Strahlungsflüssen und -indizes wurde ein Strahlungstransportmodell für Sonnenstrahlung (Cloud-J) [6] implementiert und angepasst. Da das gesamte System als effiziente Lösung für UV-Indexvorhersagen dem DWD zur Verfügung gestellt werden soll, wird besonders Wert auf eine umfassende Funktionalität bei sehr geringem Rechenaufwand gelegt. Ein wichtiger Teil der Arbeit ist daher auch die Validierung und Optimierung der Verfahren und Abläufe, um zuverlässige und qualitativ hochwertige Vorhersagen zu erstellen.</p> <p class="Default">Wir präsentieren erste Ergebnisse des von ICON-ART modellierten UV-Strahlungsflusses durch die Atmosphäre auf globaler Skala und über ausgewählten Gebieten, dessen tageszeitliche Variation, sowie den Einfluss von Wolken auf die UV-Intensität.</p> <p><strong>Anmerkung:</strong></p> <p>Dieses Projekt wird durch den Deutschen Wetterdienst im Rahmen der Extramuralen Forschung mit folgender Nummer gefördert: 4819EMF03.</p> <p><strong>Referenzen:</strong></p> <p>[1]  https://kunden.dwd.de/uvi/index.jsp</p> <p>[2]   Zängl, G., et al., The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD MPI-M: Description of the non-hydrostatic dynamical core. Q.J.R. Meteorol. Soc., 141(687), 563-579 (2014)</p> <p>[3]   McLinden, C. A., et al., Stratospheric ozone in 3-D models: A simple chemistry and the cross-tropopause flux, Journal of Geophysical Research: Atmospheres, 105(D11), 14653-14665 (2000)</p> <p>[4]  Rieger, D., et al., ICON-ART - A new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8(6), 1659-1676 (2015)</p> <p>[5]  Schröter, et al., ICON-ART 2.1: a flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations. Geosci. Model Dev., 11(10), 4043-4068 (2018)</p> <p>[6]  Prather, M.J., Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c. Geosci. Model Dev., 8(8), 2587-2595 (2015)</p>

Novel assessment of numerical forecasting model relative humidity with satellite probabilistic estimates

A novel method of comparison between an atmospheric model and satellite probabilistic estimates of relative humidity (RH) in the tropical atmosphere is presented. The method is developed to assess the Météo-France numerical weather forecasting model ARPEGE using probability density functions (PDF) of RH estimated from the SAPHIR microwave sounder. The satellite RH reference is derived by aggregating footprint-scale probabilistic RH to match the spatial and temporal resolution of ARPEGE over the April-May-June 2018 period. The probabilistic comparison is discussed with respect to a classical deterministic comparison confronting each model RH value to the reference average and using a set confidence interval. The study first documents the significant spatial and temporal variability of the reference distribution spread and shape. It warrants the need for a finer assessment at the individual case level to characterise specific situations beyond the classical bulk comparison using determinist “best” reference estimates. The probabilistic comparison allows for a more contrasted assessment than the deterministic one. Specifically, it reveals cases where the ARPEGE simulated values falling within the deterministic confidence range actually correspond to extreme departures in the reference distribution.

Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab Emirates

Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates (UAE) where extreme events like heat waves, flash floods, and dust storms are severe. Hence, accurate forecasting of quantities like surface temperatures and humidity is very important. To date, there have been few seasonal-to-annual scale verification studies with WRF at high spatial and temporal resolution. This study employs a convection-permitting scale (2.7 km grid scale) simulation with WRF with Noah-MP, in daily forecast mode, from 1 January to 30 November 2015. WRF was verified using measurements of 2 m air temperature (T2 m), 2 m dew point (TD2 m), and 10 m wind speed (UV10 m) from 48 UAE WMO-compliant surface weather stations. Analysis was made of seasonal and diurnal performance within the desert, marine, and mountain regions of the UAE. Results show that WRF represents temperature (T2 m) quite adequately during the daytime with biases ≤+1 ∘C. There is, however, a nocturnal cold bias (−1 to −4 ∘C), which increases during hotter months in the desert and mountain regions. The marine region has the smallest T2 m biases (≤-0.75 ∘C). WRF performs well regarding TD2 m, with mean biases mostly ≤ 1 ∘C. TD2 m over the marine region is overestimated, though (0.75–1 ∘C), and nocturnal mountain TD2 m is underestimated (∼-2 ∘C). UV10 m performance on land still needs improvement, and biases can occasionally be large (1–2 m s−1). This performance tends to worsen during the hot months, particularly inland with peak biases reaching ∼ 3 m s−1. UV10 m is better simulated in the marine region (bias ≤ 1 m s−1). There is an apparent relationship between T2 m bias and UV10 m bias, which may indicate issues in simulation of the daytime sea breeze. TD2 m biases tend to be more independent. Studies such as these are vital for accurate assessment of WRF nowcasting performance and to identify model deficiencies. By combining sensitivity tests, process, and observational studies with seasonal verification, we can further improve forecasting systems for the UAE.

Machine learning emulation of gravity wave drag in numerical weather forecasting

<p>We assess the value of machine learning as an accelerator for a kernel of an operational weather forecasting system, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained that produce stable and accurate results up to seasonal forecasting timescales. By training on an increased complexity version of the parameterisation scheme we build emulators that produce more accurate forecasts than the existing parameterisation scheme. Leveraging the differentiability of neural networks we generate tangent linear and adjoint versions of our parameterisation, key components in 4D-var data-assimilation. We test our tangent linear and adjoint codes within an operational-like 4D-var setup and find no degradation in skill vs hand-written tangent-linear and adjoint codes.</p>

Mechanical stability indicators derived from detailed snow cover simulations

<p>The use of numerical modeling of the snow cover in support of avalanche hazard forecasting has been increasing in the last decade. Besides field observations and numerical weather forecasting, these numerical tools provide information otherwise unavailable on the present and future state of the snow cover. In order to provide useful input for avalanche hazard assessment, different mechanical stability indicators are typically computed from simulated snow stratigraphy. Such indicators condense the wealth of information produced by snow cover models, especially when dealing with large data (e.g., large domains, high spatial resolution, ensemble forecasting). Here, we provide an overview of such indicators. Mechanical stability indicators can be classified in two types i.e., whether they are solely based on mechanical rules or whether they include additional expert rules. These indicators span different mechanical processes involved in avalanche release: failure initiation and crack propagation, for instance. The indicators rely on mechanical properties of each layer. We discuss parameterizations of mechanical properties and the associated technical implementation details. We show simplified examples of snow stratigraphy to illustrate the benefit of different stability indicators in typical situations. There is no perfect indicator to describe the instability for any situation. All indicators are sensitive to the snow cover modeling assumptions and the computation of mechanical properties and hence, require some tuning before operational use. In practice, a combination of indicators should be considered to capture the variety of avalanche situations.</p>

Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP V3.8.1 over the United Arab Emirates

Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates (UAE) where extreme events like heat waves, flash floods and dust storms are severe. Hence, accurate forecasting of quantities like surface temperatures and humidity is very important. To date, there have been few seasonal-to-annual scale verification studies with WRF at high spatial and temporal resolution. This study employs a convection-permitting scale (2.7 km grid scale) simulation with WRF-NOAHMP, in daily forecast mode, from January 01 to November 30 2015. WRF was verified using measurements of 2 m air temperature (T-2m), dew point (TD-2m), and 10 m windspeed (UV-10m) from 48 UAE surface stations. Analysis was made of seasonal and diurnal performance within the desert, marine and mountain regions of the UAE. Results show that WRF represents temperature (T-2m) quite adequately during the daytime with biases ≤ +1 ˚C. There is however a nocturnal cold bias (−1 to −4 ˚C), which increases during hotter months in the desert and mountain regions. The marine region has the lowest T-2m biases (≤−0.75 ˚C). WRF performs well regarding TD-2m, with mean biases mostly ≤ 1 ˚C. TD-2m over the marine region is overestimated though (0.75–1 ˚C), and nocturnal mountain TD-2m is underestimated (~ −2 ˚C). UV-10m performance on land still needs improvement, and biases can occasionally be large (1–2 m s−1). This performance tends to worsen during the hot months, particularly inland with peak biases reaching ~ 3 m s−1. UV-10m are better simulated in the marine region (bias ≤ 1 m s−1). There is an apparent relationship between T-2m bias and UV-10m bias, which may indicate issues in simulation of the daytime sea breeze. TD-2m biases tend to be more independent. Studies such as these are vital for accurate assessment of WRF nowcasting performance and to identify model deficiencies. By combining sensitivity tests, process and observational studies with seasonal verification, we can further improve forecasting systems for the UAE.

Weather Forecasts

Chapter 4 tells the story of numerical weather forecasting from its inception to today’s supercomputing algorithms. In 1922, Lewis Fry Richardson proposed that, since the atmosphere is subject to the laws of physics, future weather can be predicted by means of algorithmic calculations. His attempt at forecasting a single day’s weather by means of manual calculations took several months. In the late 1940s, John von Neumann resurrected Richardson’s idea and launched a project to conduct the first weather forecast by computer. The world’s first operational electronic computer – ENIAC - completed a 24-hour forecast in just one day. It appeared that accurate forecasting simply required faster computers. In 1969, Edward Lorenz discovered that tiny errors in weather measurements can accumulate during numerical forecasting to produce large errors. The so-called Butterfly Effect was alleviated by the Monte Carlo simulation method invented by Stanislaw Ulam for particle physics.