Progress and Challenges of Demand-Led Co-Produced Sub-Seasonal-to-Seasonal (S2S) Climate Forecasts in Nigeria

This paper identifies fundamental issues which prevent the effective uptake of climate information services in Nigeria. We propose solutions which involve the extension of short-range (1 to 5 days) forecasts beyond that of medium-range (7 to 15 days) timescales through the operational use of current forecast data as well as improve collaboration and communication with forecast users. Using newly available data to provide seamless operational forecasts from short-term to sub-seasonal timescales, we examine evidence to determine if effective demand-led sub-seasonal-to-seasonal (S2S) climate forecasts can be co-produced. This evidence involves: itemization of forecast products delivered to stakeholders, with their development methodology; enumeration of inferences of forecast products and their influences on decisions taken by stakeholders; user-focused discussions of improvements on co-produced products; and the methods of evaluating the performance of the forecast products.We find that extending the production pipeline of short-range forecast timescales beyond the medium-range, such that the medium-range forecast timescales can be fed into existing tools for applying short-range forecasts, assisted in mitigating the risks of sub-seasonal climate variability on socio-economic activities in Nigeria. We also find that enhancing of collaboration and communication channels between the producers and the forecast product users helps to: enhance the development of user-tailored impact-based forecasts; increases users' trusts in the forecasts; and, seamlessly improves forecast evaluations. In general, these measures lead to more smooth delivery and increase in uptake of climate information services in Nigeria.

Sensitivity Analysis of PBL Physics Schemes of WRF-ARW Model in Simulating the Tropical Cyclone ‘TITLI’ (2018) Over the Bay of Bengal

Tropical Cyclone (TC) is the most destructive weather phenomenon in the Indian sub-continent. To mitigate the destruction due to TC better prediction is needed. So, the study of sensitivity of different physical schemes in WRF-ARW model with intensification and track of TC is important. In this study, sensitivity of Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Bougeault-Lacarrere (Boulac), Medium-Range Forecast (MRF), Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2.5) and Level 3 (MYNN3) Planetary Boundary Layer (PBL) schemes are used to simulate the TC ‘Titli’ which made land fall near Palasa in North Andrha Pradesh and South Odhisha coasts at 0000 UTC of 11th October. National center for environmental prediction Global Final Reanalysis (FNL) data have been used as an initial and lateral boundary conditions. Variation of heat flux, latent heat flux and moisture flux with time for these schemes are shown which are responsible to intensify the TC. Model simulated intensity i.e., minimum central pressure, maximum sustained wind speed at the surface (10 m) and track are compared with the India Meteorological Department (IMD) estimated value. It can be specified that the Boulac, MYNN2.5 and MYNN3 schemes simulate the better intensity and track of TC ‘Titli’.

Examination of WRF-ARW Experiments Using Different Planetary Boundary Layer Parameterizations to Study the Rapid Intensification and Trajectory of Hurricane Otto (2016)

Hurricane Otto (2016) was characterised by remarkable meteorological features of relevance for the scientific community and society. Scientifically, among the most important attributes of Otto is that it underwent a rapid intensification (RI) process. For society, this cyclone severely impacted Costa Rica and Nicaragua, leaving enormous economic losses and many fatalities. In this study, a set of three numerical simulations are performed to examine the skill of model estimations in reproducing RI and trajectory of Hurricane Otto by comparing the results of a global model to a regional model using three different planetary boundary layer parameterizations (PBL). The objective is to set the basis for future studies that analyse the physical reasons why a particular simulation (associated with a certain model setup) performs better than others in terms of reproducing RI and trajectory. We use the regional model Weather Research and Forecasting—Advanced Research WRF (WRF-ARW) with boundary and initial conditions provided by the Global Forecast System (GFS) analysis (horizontal resolution of 0.5 degrees). The PBL used are the Medium Range Forecast, the Mellor-Yamada-Janjic (MYJ), and the Yonsei University (YSU) parameterizations. The regional model is run in three static domains with horizontal grid spacing of 27, 9 and 3 km, the latter covering the spacial extent of Otto during the simulation period. WRF-ARW results improve the GFS forecast, in almost every aspect evaluated in this study, particularly, the simulated trajectories in WRF-ARW show a better representation of the cyclone path and movement compared to GFS. Even though the MYJ experiment was the only one that exhibited an abrupt 24-h change in the storm’s surface wind, close to the 25-knot threshold, the YSU scheme presented the fastest intensification, closest to reality.

The role of arctic forecast errors in the evolution of northern extra-tropical forecast skill

<p><br>Increases in extra-tropical numerical weather prediction (NWP) skill over the last decades have been well documented. The role of the Arctic, defined here as the area north of 60N, in driving (or slowing) this improvement has however not been systematically assessed. To investigate this question, spatial patterns of changes in medium-range forecast error of ECMWF’s Integrated Forecast System (IFS) are analysed both for deterministic and ensemble forecasts. The robustness of these patterns is evaluated by comparing results for different parameters and levels, and by comparing them with the respective changes in ERA5 forecasts, which are based on a ‘frozen’ model version. In this way the effect of different atmospheric variability on the estimation of skill improvement can be minimized. It is shown to what extent the strength of the polar vortex as measured by the Arctic and North-Atlantic Oscillation (AO, NAO) influences the magnitude of forecast errors. Results may indicate whether recent and future changes in these indices, possibly driven in part by sea-ice decline, could systematically affect the longer-term evolution of medium-range forecast skill.</p>

Towards operational post-processing of probabilistic temperature forecasts at MeteoSwiss

<p>MeteoSwiss is currently developing a post-processing suite for the territory of Switzerland. The system aims to provide optimized multi-variable (i.e. temperature, precipitation, wind and cloud cover), spatial and probabilistic predictions. The system will combine information in a seamless manner from the in-house short range and regional (COSMO-E/1) of 1 resp. 2 km resolution and the medium range ECMWF IFS NWP systems. At the example of probabilistic temperature forecasts, this contribution discusses recent advances and experiences at developing, applying and operationalizing non-homogenous Gaussian regression, also known as ensemble model output statistics (EMOS).</p><p>Over the complex terrain of Switzerland, postprocessing leads to a substantial improvement of temperature forecasts by up to 30% in terms of CRPS with respect to elevation-corrected direct model output (DMO) even by a basic EMOS only relying on DMO of temperature. Incorporating suitable predictors, such as the atmospheric boundary layer height, leads to a further gain in forecast quality. Results also show that combining high- (COSMO-E) and coarse-resolution (IFS) NWP output can not only provide a seamless medium-range forecast, but also further increase prediction skill during the time horizon when both models are available. Finally, we discuss first attempts to produce high-resolution spatial PP fields for arbitrary locations by exploiting a global EMOS framework with multiple static (e.g. geographic characteristics) and dynamic predictors derived from NWP data.</p>

La représentation des surfaces continentales pour la prévision hydrologique

La modélisation hydrométéorologique initiée par Joël Noilhan permet aujourd'hui d'anticiper les risques de crues sur plusieurs jours, l'évolution de la ressource en eau en France sur plusieurs mois et de projeter les tendances sur le XXIe siècle. Pour cela, il a fallu intégrer des processus sous mailles dans le schéma de surface Isba, car ils sont à l'origine de la genèse d'écoulements préférentiels, et affiner la description de la physiographie. Un des co-bénéfices les plus marquants a été la production d'une réanalyse des variables météorologiques de surface sur la France, aujourd'hui disponible sur plus de 60 ans. Les collaborations initiées avec les hydrologues et acteurs de l'eau se sont encore renforcées, afin de co-construire les modèles de prévisions hydrométéorologiques de demain. The hydrometeorological modeling initiated by Joël Noilhan leads today to short- and medium-range forecast of flood risks, seasonal forecasts of the evolution of the water resource in France and projection of its evolution during the 21st century. To do so, it was necessary to integrate subgrid processes in the land surface scheme ISBA, as they generate preferential flow, and to refine physiographic datasets. One of the most significant co-benef its is the production of a reanalysis of near-surface meteorological variables over France now available over more than 60 years. The initial collaboration with hydrologists and stakeholders has now been strengthened in order to co-design future hydrometeorological forecast models.