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Accueil du site → Doctorat → États-Unis → 2021 → Improving Daily and Sub-seasonal Precipitation Forecasts in Arid Regions Through Convective-Permitting Modeling and Data Assimilation

University of Arizona (2021)

Improving Daily and Sub-seasonal Precipitation Forecasts in Arid Regions Through Convective-Permitting Modeling and Data Assimilation

Risanto, Christoforus Bayu

Titre : Improving Daily and Sub-seasonal Precipitation Forecasts in Arid Regions Through Convective-Permitting Modeling and Data Assimilation

Auteur : Risanto, Christoforus Bayu

Université de soutenance : University of Arizona

Grade : Doctor of Philosophy (PhD) 20221

Résumé partiel
Precipitation in arid and semi-arid regions is important for both human and natural systems, but the extreme weather events associated with the precipitation can pose threats to the growing population in the regions, especially due to the changing of climate. Thus, there is a need for reliable weather forecast systems that are able to provide early warnings to reduce fatalities and socio-economic loss. This dissertation demonstrates two forecasting techniques that are able to improve the current operational forecast systems to predict extreme precipitation events in arid and semi-arid environments. The first technique is utilizing a data assimilation system in convective-permitting models. The data assimilation implements the ensemble Kalman filter scheme that integrates precipitable water vapor (PWV) data derived from Global Positioning System (GPS) sensors into a numerical weather prediction (NWP) model and updates the modeled state variables sequentially. The updated modeled PWV is expected to maintain the errors between the model and the observations low at the model initial condition. This potentially generates more accurate weather forecasts since the initial model biases is minimized. We implemented this technique during the North American monsoon (NAM) GPS Hydrometeorological Network field campaign in summer 2017 over northwest Mexico. The GPS-PWV data from the field campaign was assimilated into a 30-member ensemble convective-permitting (2.5 km) model. The results show that assimilating GPS-PWV improves the initial condition of the modeled PWV, most unstable convective available potential energy (MUCAPE), and 2-meter dewpoint temperature (Td2). This also leads to an improvement in capturing nocturnal convection of mesoscale convective systems (MCSs ; after 0300 UTC) and to an increase by 0.1 mm h-1 in subsequent precipitation during the 0300-0600 UTC period relative to no assimilation of the GPS-PWV (NODA) over the area with relatively more observation sites. The results demonstrate that this technique could be implemented over areas where traditional observations for data assimilation, such as radar and radiosonde, are not available. The second technique is applying convective-permitting (CP) modeling to a sub-seasonal weather forecast model. In this technique the model’s horizontal grids are downscaled from 80 km to 4 km that is a convective-permitting scale, in which precipitation can be represented and resolved explicitly without any convective scheme

Mots clés : Convective-resolving models Data Assimilation Forecast verification skills Numerical Weather Prediction Sub-seasonal forecasts

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Page publiée le 14 novembre 2022