Evaluation of a WRF model in forecasting extreme rainfall in the urban data-scarce coastal city of Alexandria, Egypt

2021 ◽  
Author(s):  
Adele Young ◽  
Biswa Bhattacharya ◽  
Emma Daniels ◽  
Chris Zevenbergen
Keyword(s):  
High Resolution ◽  
Boundary Conditions ◽  
Temporal Variability ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Small Scale ◽  
Lead Times ◽  
Spatial And Temporal Variability ◽  
Urban Drainage

<p>High-resolution precipitation models are essential to forecast urban pluvial floods. Global Numerical Weather Prediction Models (NWPs) are considered too coarse to accurately forecast flooding at the city scale. High-resolution radar nowcasting can be either unavailable or insufficient to forecast at the required lead-times.  Downscaling models are used to increase the resolution and extend forecast by several days when initialised with global NWPs. However, resolving weather processes at smaller spatial scales and sub-daily temporal resolutions has its challenges and does not necessarily result in more accurate forecast but instead only increase the computational requirements. Additionally, in ungauged regions, forecast verification is a challenge as in-situ measurements and radar estimates remain scarce or non-existent. This research evaluates the ability of a dynamically downscaled WRF model to capture the spatial and temporal variability of rainfall suitable for an urban drainage flood forecast model and evaluated against IMERG Global Precipitation Model (GPM) Satellite Precipitation Products (SPPs).<br> A WRF model was set-up with one-way nesting, three nested domains at horizontal grid resolutions 10km, 3.33km and 1km, a 1hourly temporal output, a spin-up time of 12 hours and evaluated at different lead times up to 48 hrs. The analysis was performed for three (3)  winter frontal systems during the period 2015-2019 in the highly urbanised coastal Mediterranean city of Alexandria in Egypt which experiences floods from extreme precipitation. The Global Forecast System (GFS), and European Centre for Medium Range (ECMWF) forecast were used as initial and lateral boundary conditions. <br>Initial results indicate the WRF models could capture extreme rainfall for all events. There is some agreement with the IMERG data and the model correctly forecasted a decrease in rainfall as the systems transition from coastal to inland areas. In general, GFS and ECMWF initialised WRF models overestimated rainfall estimates compared to IMERG data. Differences in GFS and ECMWF initialised models (multi-model approach) highlight the sensitivity of models to initial and boundary conditions and emphasises the need for post-processing and data assimilation when possible to generate accurate small-scale features. A study such as this provides knowledge for understanding, future applications and limitations of using Quantitative Precipitation Forecasts (QPFs) in urban drainage models. Additionally, the potential use of IMERG GPM to verify spatial and temporal variability of forecast in ungauged and data-scarce regions. Future analysis will evaluate the skill of ensembles precipitation systems in characterising forecast uncertainty in such applications. </p>

scholarly journals Can Lagrangian Extrapolation of Radar Fields Be Used for Precipitation Nowcasting over Complex Alpine Orography?

2012 ◽  
Vol 27 (1) ◽  
pp. 28-49 ◽  
Author(s):  
Pradeep V. Mandapaka ◽  
Urs Germann ◽  
Luca Panziera ◽  
Alessandro Hering
Keyword(s):  
High Resolution ◽  
Comprehensive Evaluation ◽  
Weather Prediction ◽  
Summer Rainfall ◽  
Numerical Weather Prediction Model ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Lead Times ◽  
Radar Echo ◽  
Short Term Forecasting

Abstract In this study, a Lagrangian radar echo extrapolation scheme (MAPLE) was tested for use in very short-term forecasting of precipitation over a complex orographic region. The high-resolution forecasts from MAPLE for lead times of 5 min–5 h are evaluated against the radar observations for 20 summer rainfall events by employing a series of categorical, continuous, and neighborhood evaluation techniques. The verification results are then compared with those from Eulerian persistence and high-resolution numerical weather prediction model [the Consortium for Small-scale Modeling model (COSMO2)] forecasts. The forecasts from the MAPLE model clearly outperformed Eulerian persistence forecasts for all the lead times, and had better skill compared to COSMO2 up to lead time of 3 h on average. The results also showed that the predictability achieved from the MAPLE model depends on the spatial structure of the precipitation patterns. This study is a first implementation of the MAPLE model over a complex Alpine region. In addition to comprehensive evaluation of precipitation forecast products, some open questions related to the nowcasting of rainfall over a complex terrain are discussed.

scholarly journals Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

2017 ◽  
Vol 21 (7) ◽  
pp. 3859-3878 ◽  
Author(s):  
Elena Cristiano ◽  
Marie-Claire ten Veldhuis ◽  
Nick van de Giesen
Keyword(s):  
Temporal Variability ◽  
Urban Areas ◽  
Rainfall Variability ◽  
Urban Environments ◽  
Hydrological Processes ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Hydrological Response ◽  
Weather Radars ◽  
New Instruments

Abstract. In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

scholarly journals The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 1: Description of the limited-area atmospheric chemistry model COSMO/MESSy

2012 ◽  
Vol 5 (1) ◽  
pp. 87-110 ◽  
Author(s):  
A. Kerkweg ◽  
P. Jöckel
Keyword(s):  
Boundary Conditions ◽  
Atmospheric Chemistry ◽  
General Circulation ◽  
Weather Prediction ◽  
Model System ◽  
Small Scale ◽  
Limited Area ◽  
Tracer Transport ◽  
Chemistry Research ◽  
Supplementary Document

Abstract. The numerical weather prediction model of the Consortium for Small Scale Modelling (COSMO), maintained by the German weather service (DWD), is connected with the Modular Earth Submodel System (MESSy). This effort is undertaken in preparation of a new, limited-area atmospheric chemistry model. Limited-area models require lateral boundary conditions for all prognostic variables. Therefore the quality of a regional chemistry model is expected to improve, if boundary conditions for the chemical constituents are provided by the driving model in consistence with the meteorological boundary conditions. The new developed model is as consistent as possible, with respect to atmospheric chemistry and related processes, with a previously developed global atmospheric chemistry general circulation model: the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. The combined system constitutes a new research tool, bridging the global to the meso-γ scale for atmospheric chemistry research. MESSy provides the infrastructure and includes, among others, the process and diagnostic submodels for atmospheric chemistry simulations. Furthermore, MESSy is highly flexible allowing model setups with tailor made complexity, depending on the scientific question. Here, the connection of the MESSy infrastructure to the COSMO model is documented and also the code changes required for the generalisation of regular MESSy submodels. Moreover, previously published prototype submodels for simplified tracer studies are generalised to be plugged-in and used in the global and the limited-area model. They are used to evaluate the TRACER interface implementation in the new COSMO/MESSy model system and the tracer transport characteristics, an important prerequisite for future atmospheric chemistry applications. A supplementary document with further details on the technical implementation of the MESSy interface into COSMO with a complete list of modifications to the COSMO code is provided.

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

scholarly journals Regional data assimilation of multi-spectral MOPITT observations of CO over North America

2015 ◽  
Vol 15 (12) ◽  
pp. 6801-6814 ◽  
Author(s):  
Z. Jiang ◽  
D. B. A. Jones ◽  
J. Worden ◽  
H. M. Worden ◽  
D. K. Henze ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Boundary Conditions ◽  
North American ◽  
A Priori ◽  
Sensitivity Analyses ◽  
Small Scale ◽  
Lateral Boundary ◽  
Free Troposphere ◽  
Lateral Boundary Conditions

Abstract. Chemical transport models (CTMs) driven with high-resolution meteorological fields can better resolve small-scale processes, such as frontal lifting or deep convection, and thus improve the simulation and emission estimates of tropospheric trace gases. In this work, we explore the use of the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system with the nested high-resolution version of the model (0.5° × 0.67°) to quantify North American CO emissions during the period of June 2004–May 2005. With optimized lateral boundary conditions, regional inversion analyses can reduce the sensitivity of the CO source estimates to errors in long-range transport and in the distributions of the hydroxyl radical (OH), the main sink for CO. To further limit the potential impact of discrepancies in chemical aging of air in the free troposphere, associated with errors in OH, we use surface-level multispectral MOPITT (Measurement of Pollution in The Troposphere) CO retrievals, which have greater sensitivity to CO near the surface and reduced sensitivity in the free troposphere, compared to previous versions of the retrievals. We estimate that the annual total anthropogenic CO emission from the contiguous US 48 states was 97 Tg CO, a 14 % increase from the 85 Tg CO in the a priori. This increase is mainly due to enhanced emissions around the Great Lakes region and along the west coast, relative to the a priori. Sensitivity analyses using different OH fields and lateral boundary conditions suggest a possible error, associated with local North American OH distribution, in these emission estimates of 20 % during summer 2004, when the CO lifetime is short. This 20 % OH-related error is 50 % smaller than the OH-related error previously estimated for North American CO emissions using a global inversion analysis. We believe that reducing this OH-related error further will require integrating additional observations to provide a strong constraint on the CO distribution across the domain. Despite these limitations, our results show the potential advantages of combining high-resolution regional inversion analyses with global analyses to better quantify regional CO source estimates.

Assessing spatio-temporal variability and biases of climate models

2020 ◽  
Author(s):  
Nedjeljka Žagar
Keyword(s):  
Temporal Variability ◽  
Prediction Models ◽  
Climate Model ◽  
Weather Prediction ◽  
Ocean Model ◽  
Spatial And Temporal Variability ◽  
Dynamical Regime ◽  
Temporal Variance ◽  
Slab Ocean ◽  
Spatio Temporal

<div>Atmospheric spatial and temporal variability are closely related with the former being relatively well observed compared to the latter. The former is also regularly assessed in the validation of numerical weather prediction models while the latter is more difficult to estimate. Likewise, thermodynamical fields and circulation are closely coupled calling for an approach that considers them simultaneously.  </div> <div>In this contribution, spatio-temporal variability spectra of the four major reanalysis datasets are discussed and applied for the validation of a climate model prototype.  A relationship between deficiencies in simulated variability and model biases is derived. The underlying method includes dynamical regime decomposition thereby providing a better understanding of the role of tropical variability in global circulation. </div> <div>Results of numerical simulations are validated by a 20th century reanalysis. A climate model was forced either with the prescribed SST or with a slab ocean model that updates SST in each forecast step.  Scale-dependent validation shows that missing temporal variance in the model relative to verifying reanalysis increases as the spatial scale reduces that appears associated with an increasing lack of spatial variance at smaller scales. Similar to variability, bias is strongly scale dependent; the larger the scale, the greater the bias. Biases present in the SST-forced simulation increase in the simulation using the slab ocean. The comparison of biases computed as a systematic difference between the model and reanalysis and between the SST-forced model and slab-ocean model (a perfect-model scenario) suggests that improving the atmospheric model increases the variance in the model on synoptic and subsynoptic scales but large biases associated with a poor SST remain at planetary scales.</div> <p> </p>

High-resolution dataset assessing methane concentrations and modelling the carbon dynamics within Europe's second largest delta, the Danube River Delta

2020 ◽  
Author(s):  
Anna Canning ◽  
Arne Körtzinger
Keyword(s):  
High Resolution ◽  
Dissolved Inorganic Carbon ◽  
Hot Spot ◽  
River Delta ◽  
Danube River ◽  
The Black Sea ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Diel Variation ◽  
The Danube River

<p>Wetlands are known to be significant sources for CH<sub>4</sub>, yet vary between potential sources and sinks for CO<sub>2</sub>. However, in regards to the budgets and processes, they are still considered to have high uncertainties, inconsistencies and a general lack of data overall. One key wetland region in Europe is the Danube River Delta. It is the second largest delta in Europe, consisting of the vastest compact reed bed zone in the world, intertwined with rivers, lakes and channels. It is sourced with water from a drainage basin of 817,000 km<sup>2</sup>, with the Danube River originating in Germany before travelling 2,857 km to the Black Sea. However, considering the potential pollution effects within this terminal zone, as well as the delta being one of the most important wetlands in Europe for its ecological value alone (and therefore fragile), few studies have focused on the dynamics within the carbon cycle. During 2017, three field campaigns across three seasons measured high resolution, small-scale spatial and temporal variability for pCO<sub>2</sub>, CH<sub>4</sub>, O<sub>2</sub> and ancillary parameters within the lakes, rivers and channels with the use of a surface water flow-through package. Given the flexibility of the system, we were able to conduct day-night cycles and extensive mapping transects. We discovered day-night cycles showing significant variation of CH<sub>4</sub> concentrations within the lakes and channels, as well ‘hot spot’ anomalies showing potential ground water sourcing and extreme CH<sub>4</sub> concentrations flowing in from the reed beds. Although reasoning for supersaturation in surface waters are under continuous debate, we conclude a potential reason for such dynamic diel variation within the lake may be due to biomass decomposition and extensive macrophyte concentrations creating a temporarily anoxic zone during the day with mixing during the night, such as previously suggested. On top of this, with the use of discrete data collected from the same water source simultaneously, we were able to model alkalinity, dissolved inorganic carbon and pH to examine both 24 h cycles across lakes and day-night dynamics, giving an in-depth glimpse into the carbonate system. Through the extensive mapping, we successful extracted diel variations for pCO<sub>2</sub> and the carbonate species across the lakes with the use of just day-light data, allowing for spatial and temporal variations to be distinguished. We confirm the boundaries between channels and lakes are intertwined as much as they are with the wetlands, and how small extreme anomalies can only begin to be explained with such high-resolution data, even more so in combination with modelled data.</p>

scholarly journals High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

2015 ◽  
Vol 8 (8) ◽  
pp. 2645-2653 ◽  
Author(s):  
C. G. Nunalee ◽  
Á. Horváth ◽  
S. Basu
Keyword(s):  
High Resolution ◽  
Vortex Shedding ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wrf Model ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Nwp Model ◽  
Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

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