High-resolution Forecasts Research Articles

What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

Oil spill trajectory prediction with high-resolution ocean currents

ABSTRACT Simulated ocean currents from ocean circulation models along with the surface winds are generally used to force an oil spill trajectory model. Ocean circulation is a key factor in determining the drift of the spilled marine pollutants. The simulations of the drift pattern of spilled oil, when forced by the ocean currents from two ocean models, are presented here. Merchant Vessel (MV) Rak sunk at 72.4865°E, 18.7715°N, on 4 August 2011. As per the information from Indian Coast Guard, approximately 122.5 tons of fuel oil was spilled from the vessel from 5 August 2011 to 12 August 2011. An oil spill trajectory model, General NOAA Operational Modeling Environment (GNOME), was used to simulate the oil drift pattern from MV Rak from 1000 hours of 5 August 2011 to 1300 hours of 12 August 2011. GNOME was forced with winds from European Centre for Medium Range Weather Forecast (ECMWF) and ocean currents from Indian Ocean Forecasting System (INDOFOS) and High-resolution Operational Ocean Forecasting and reanalysis System (HOOFS). We found that the oil drift pattern obtained when forced with ocean currents from HOOFS was in better agreement with the actual track, compared to the one obtained while using INDOFOS currents.

Evaluation of Tropical Cyclone Genesis Precursors with Relative Operating Characteristics (ROC) in Highresolution Ensemble Forecasts: Hurricane Ernesto

ABSTRACT Identifying the environmental conditions that control tropical cyclone (TC) genesis is a challenging problem. This study examines a new method to evaluate the precursors of TC genesis using high-resolution ensemble forecasts and relative operating characteristic (ROC) diagrams. With an advanced research version of the Weather Research and Forecasting (WRF) model, high-resolution ensemble forecasts (at 5 km horizontal resolution) are conducted in various configurations using a bred vector method to form a set of 140 ensemble members for predicting Hurricane Ernesto’s genesis. Basic evaluation shows that high-resolution ensemble forecasts are able to predict well-developed TCs, whereas the NCEP Global Ensemble Forecast System (GEFS) fails to do so. This set of 140 ensemble members is employed to study the precursors of Hurricane Ernesto’s genesis by contrasting the genesis and nongenesis cases. Specifically, ROC curves, composite figures for genesis and nongenesis cases, and Kolmogorov-Smirnov tests are applied to characterize the relationship between important environmental parameters near the beginning of the simulation and genesis likelihood 15-18 h later. It is found that moist conditions at 850 hPa, vertical wind shear, the strength of the 850 hPa pre existing wave, and upper-level warming play notable roles in Ernesto’s genesis.

Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach

Abstract. Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.

Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

Towards robust pan-European storm surge forecasting

Operational forecasting systems are important for disaster risk reduction. In this work we implement a coupled storm surge and tidal model on an unstructured grid over Europe towards the development of a pan-European Storm Surge Forecasting System (EU-SSF). The skill to predict tidal, surge and total water levels was evaluated based on measurements from 208 tidal gauge stations. Results show satisfactory performance for the two atmospheric forcing datasets tested, a High Resolution Forecast and ERA-INTERIM reanalysis, both provided by the European Center for Medium range Weather Forecast. For tidal predictions, the total RSS is equal to 0.197 m, lower than the values estimated by the global tidal model FES2004, and outperformed only by FES2012 (RSS = 0.05 m), which however is a product of data assimilation. Storm surge validation results show good predictive skill, with 0.04 m < RMSE < 0.21 m and %RMSE within 4%–22%. Coupling with tides results in improved storm surge level predictions, with RMSE reducing by up to 0.033 m. The areas benefiting most from the coupling are the North Sea and the English Channel, resulting in up to 2% reduction of the %RMSE. Increasing the resolution of atmospheric forcing also improves the predictive skill, leading to a reduction of RMSE up to 0.06 m in terms of the extremes, especially in shallow areas where wind is the main driver for surge production. We propose a setup for operational pan-European storm surge forecasting combining tidal levels from the FES2012 model and storm surge residuals from the EU-SSF setup which couples meteorological and astronomic tides.

A study on the capability of the NCEP-CFS model in simulating the frequency and intensity of high-intensity rainfall events over Indian region in the high and low resolutions

In the current perception of an increase in extreme precipitation events in a developing and densely populated country like India and the demands of high resolution forecast runs are high, the present study compares the statistical skill of free runs from an operational climate model run in two horizontal resolutions in simulating the frequency and intensity of extreme rainfall events over Indian region. The operational climate model is a version of the National Center for Environmental Prediction (NCEP, USA) Climate Forecast System (CFS) version 2. It is run at two horizontal resolutions: CFST126 and CFST382, which is used for seasonal and extended range prediction in real-time in the India Meteorological Department’s (IMD) operational forecast framework. From the analysis of departure and bias of both the components of the model with respect to India Meteorological Department’s (IMD) observation, it is observed that marked dissimilarity in simulating high-intensity rain events exists between this two versions of the same model which should be hypothetically same. Both the models capture intensity and frequency differently. The main conclusions are (a) CFST126 free run gives better estimates of the frequency of rainfall events compared to CFST382, (b) CFST382 free run gives better estimates of the intensity of rainfall events compared to CFST126. These discrepancies indicate the resolution dependence of the statistics of extreme event, which should be statistically corrected and a multi-resolution ensemble version runs of CFSv2 has to be used for operational outlooks of the extreme events.

LANFEX: A Field and Modeling Study to Improve Our Understanding and Forecasting of Radiation Fog

AbstractFog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.

Application of ensemble Kalman filter in WRF model to forecast rainfall on monsoon onset period in South Vietnam

Application of ensemble Kalman filter in WRF model to forecast rainfall on monsoon onset period in South Vietnam

Global modeling of tropical cyclone storm surges using high-resolution forecasts

We assess the suitability of ECMWF Integrated Forecasting System (IFS) data for the global modeling of tropical cyclone (TC) storm surges. We extract meteorological forcing from the IFS at a 0.225° horizontal resolution for eight historical TCs and simulate the corresponding surges using the global tide and surge model. Maximum surge heights for Hurricanes Irma and Sandy are compared with tide gauge observations, with R2-values of 0.86 and 0.74 respectively. Maximum surge heights for the other TCs are in line with literature. Our case studies demonstrate that a horizontal resolution of 0.225° is sufficient for the large-scale modeling of TC surges. By upscaling the meteorological forcing to coarser resolutions as low as 1.0°, we assess the effects of horizontal resolution on the performance of surge modeling. We demonstrate that coarser resolutions result in lower-modeled surges for all case studies, with modeled surges up to 1 m lower for Irma and Nargis. The largest differences in surges between the different resolutions are found for the TCs with the highest surges. We discuss possible drivers of maximum surge heights (TC size, intensity, and coastal slope and complexity), and find that coastal complexity and slope play a more profound role than TC size and intensity alone. The highest surges are found in areas with complex coastlines (fractal dimension > 1.10) and, in general, shallow coastlines. Our findings show that using high-resolution meteorological forcing is particularly beneficial for areas prone to high TC surges, since these surges are reduced the most in coarse-resolution datasets.

Evaluating high-resolution forecasts of atmospheric CO and CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; from a global prediction system during KORUS-AQ field campaign

Abstract. Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses as well as two higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and 9 km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16 km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of −19.2 (16 km), −16.7 (9 km), and −20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (dCO/dCO2=9 ppbv ppmv−1) compared to the West Sea (dCO/dCO2=28 ppbv ppmv−1). This “combustion signature contrast” is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8–12 ppbv ppmv−1, the West Sea: ∼30 ppbv ppmv−1) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64–0.90, the West Sea: ∼0.80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients (∼0.4 ppmv hPa−1 for CO2 and 3.5 ppbv hPa−1 for CO) in the morning samples than observed (∼0.25 ppmv hPa−1 for CO2 and 1.7 ppbv hPa−1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts.

On adjoint method based atmospheric emission source tracing

The utmost important task for air pollution prevention and control is to identify the source of pollution such that targeted management and governance can be greatly enhanced. In this paper, we have develoepd an air quality forecasting system called “the national air quality high-resolution forecast and pollution control decision support system (NARS)”, which integrates atmospheric environment monitoring and analysis, meteorological numerical forecasting, emission source inversion, air quality forecasting, meteorological and atmospheric chemical data assimilation, emission sources tracing, profit and loss assessment together with dynamic optimal control. The system could be applied to perform the closed-loop prevention and control for atmospheric pollution by monitoring, forecasting, assimilation, traceability, emission source inversion and optimal control, and the system stands a great promise of providing a solution for current air pollution prevention and control strategy. In the NARS, a CAMx based adjoint model was developed, and was further demonstrated to perform dynamic inversion and grid-based quantitative emission source tracing. Our results show that the system can trace quickly and quantitatively both spatial and temporal distributions of emission sources and their contribution rates resulting from severe air pollution for certain target area for the coming 7 days. As an application of the NARS, the emission inversion, meteorological field simulation, air quality forecasting and grid traceability were studied for the time period from September 2016 to March 2017 in Beijing. Compared with the observation of national control station in Beijing-Tianjin-Hebei region (BTH), the results showed that the forecast accuracies of the pollution processes, the pollution level and the pollutant concentration are close to 100%, 88.8% and 84.7%, respectively, and the correlation coefficient between the modeling and observation is 0.81. The results of grid traceability showed that the emissions of PM2.5 pollution in the main urban area of Beijing are mainly from the air pollutant transmission channel in the southwest of. The emission contributions of BTH plus the surrounding areas are respectively 66%, 29% and 5%. The 19% PM2.5 emissions of BTH have resulted in 80% Beijing PM2.5 concentration in heavily polluted and severely polluted days. Among them, 9% Beijing local emissions account for 63% PM2.5, and 10% Hebei emissions accounted for 17%. The 26% BTH emissions have resulted in 80% PM2.5 in Beijing’s main city for slightly or heavily polluted days. Furthermore, Beijing emissions accounting for 9% of BTH emissions have contributed 61%; meanwhile, Hebei emissions accounting for 15% of BTH emissions have contributed the 18% of the pollution; finally, Tianjin 2% accouts for the leaving 1%. The emissions leading to the PM2.5 pollution in Beijing main city mainly distribute in the city zone and the sourthern of Beijing, some districts or counties of Baoding and Shijiazhuang. The top six districts or counties accounting for 48% pollutants are located in Beijing, while the top 20 districts or counties contribute 73%. Here, using the the adjoint model developed, we are able to analyze synchronously the emisison apportionment of the modeling results through combining the dynamic inversion and the monitoring emission inventories. Using both profit-loss assessment and natural cybernetics, the NARS is able to locate the emisison sources for optimal emisison control during a heavy air pollution episode.

Conditional summertime day-ahead solar irradiance forecast

We investigated the accuracy of numerical weather prediction (NWP)-based global horizontal irradiance (GHI) and clear-sky index forecasting over southern Nevada. Accurate forecasts of solar irradiance are required for electric utilities to economically integrate substantial amounts of solar power into their power generation portfolios. Solar irradiance forecasting can enhance the value of renewable energy by anticipating fluctuations in these variable resources. Summertime cloud variability depends largely on the combination of tropical and extratropical synoptic-scale forcing, most of which is observable, predictable, and highly related to the North American Monsoon moisture surge events. We used high-resolution realtime NWP output based on the weather and research forecasting (WRF) model to study the ability of the model to deliver day-ahead GHI and clear-sky index forecasts for a the National Renewable Energy Laboratory (NREL)-University of Nevada site, located in Las Vegas, Nevada. High-resolution forecast products were obtained from the Desert Research Institute (DRI) archived real-time numerical weather forecasting products. Results showed the importance of developing a site-specific seasonal and weather-dependent model output statistics (MOS) approach to improving forecast accuracy, which removes the bias and reduces the overall relative root-mean – square error (rRMSE) of GHI by as much as 6%, when compared to the uncorrected model output; improving forecast accuracy is obtained by adding information that relates regional-scale circulation patterns driving cloudiness, hence irradiance variability to the target area. We show the seasonal dependence of the NWP forecast accuracy and demonstrate that intelligent weather functions provide a pathway to improve accuracy of solar forecasts further.

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/∼13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

Estimating the snowfall limit in alpine and pre-alpine valleys: A local evaluation of operational approaches

The snowfall limit has important implications for different hazardous processes associated with prolonged or heavy precipitation such as flash floods, rain-on-snow events and freezing precipitation. To increase preparedness and to reduce risk in such situations, early warning systems are frequently used to monitor and predict precipitation events at different temporal and spatial scales. However, in alpine and pre-alpine valleys, the estimation of the snowfall limit remains rather challenging. In this study, we characterize uncertainties related to snowfall limit for different lead times based on local measurements of a vertically pointing micro rain radar (MRR) and a disdrometer in the Zulg valley, Switzerland. Regarding the monitoring, we show that the interpolation of surface temperatures tends to overestimate the altitude of the snowfall limit and can thus lead to highly uncertain estimates of liquid precipitation in the catchment. This bias is much smaller in the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which integrates surface station and remotely sensed data as well as outputs of a numerical weather prediction model. To reduce systematic error, we perform a bias correction based on local MRR measurements and thereby demonstrate the added value of such measurements for the estimation of liquid precipitation in the catchment. Regarding the nowcasting, we show that the INCA system provides good estimates up to 6 h ahead and is thus considered promising for operational hydrological applications. Finally, we explore the medium-range forecasting of precipitation type, especially with respect to rain-on-snow events. We show for a selected case study that the probability for a certain precipitation type in an ensemble-based forecast is more persistent than the respective type in the high-resolution forecast (HRES) of the European Centre for Medium Range Weather Forecasts Integrated Forecasting System (ECMWF IFS). In this case study, the ensemble-based forecast could be used to anticipate such an event up to 7–8 days ahead, whereas the use of the HRES is limited to a lead time of 4–5 days. For the different lead times investigated, we point out possibilities of considering uncertainties in snowfall limit and precipitation type estimates so as to increase preparedness to risk situations.

Ensemble Kalman Filter Data Assimilation for the Model for Prediction Across Scales (MPAS)

A global atmospheric analysis and forecast system is constructed based on the atmospheric component of the Model for Prediction Across Scales (MPAS-A) and the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The system is constructed using the unstructured MPAS-A Voronoi (nominally hexagonal) mesh and thus facilitates multiscale analysis and forecasting without the need for developing new covariance models at different scales. Cycling experiments with the assimilation of real observations show that the global ensemble system is robust and reliable throughout a one-month period for both quasi-uniform and variable-resolution meshes. The variable-mesh assimilation system consistently provides higher-quality analyses than those from the coarse uniform mesh, in addition to the benefits of the higher-resolution forecasts, which leads to substantial improvements in 5-day forecasts. Using the fractions skill score, the spatial scale for skillful precipitation forecasts is evaluated over the high-resolution area of the variable-resolution mesh. Skill decreases more rapidly at smaller scales, but the variable mesh consistently outperforms the coarse uniform mesh in precipitation forecasts at all times and thresholds. Use of incremental analysis updates (IAU) greatly decreases high-frequency noise overall and improves the quality of EnKF analyses, particularly in the tropics. Important aspects of the system design related to the unstructured Voronoi mesh are also investigated, including algorithms for handling the C-grid staggered horizontal velocities.

Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

The importance of forecasting regional wind power ramping: A case study for the UK

In recent years there has been a significant change in the distribution of wind farms in Great Britain, with a trend towards very large offshore farms clustered together in zones. However, there are concerns these clusters could produce large ramping events on time scales of less than 6 h as local meteorological phenomena simultaneously impact the production of several farms. This paper presents generation data from the wind farms in the Thames Estuary (the largest cluster in the world) for 2014 and quantifies the high frequency power ramps. Based on a case study of a ramping event which occurred on 3rd November 2014, we show that due to the large capacity of the cluster, a localised ramp can have a significant impact on the cost of balancing the power system on a national level if it is not captured by the forecast of the system operator. The planned construction of larger offshore wind zones will exacerbate this problem. Consequently, there is a need for accurate regional wind power forecasts to minimise the costs of managing the system. This study shows that state-of-the-art high resolution forecast models have capacity to provide valuable information to mitigate this impact.

The HIRLAM fast radiation scheme for mesoscale numerical weather prediction models

Abstract. This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system is ongoing. In a HARMONIE-AROME 3-D forecast experiment we have shown that the frequency of the call for the radiation parametrization and choice of the parametrization scheme makes a difference to the surface radiation fluxes and changes the spatial distribution of the vertically integrated cloud cover and precipitation.

From urban meteorology, climate and environment research to integrated city services

Accelerating growth of urban populations, especially in developing countries, has become a driving force of human development. Crowded cities are centres of creativity and economic progress, but polluted air, flooding and other climate impacts, means they also face major weather, climate and environment-related challenges. Increasingly dense, complex and interdependent urban systems leave cities vulnerable: a single extreme event can lead to a widespread breakdown of a city's infrastructure often through domino effects. The World Meteorological Organization (WMO) recognizes that rapid urbanization necessitates new types of services which make the best use of science and technology and considers the challenge of delivering these as one of the main priorities for the meteorological community. Such Integrated Urban Weather, Environment and Climate Services should assist cities in facing hazards such as storm surges, flooding, heat waves, and air pollution episodes, especially in changing climates. The aim is to build urban services that meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts, multi-hazard early warning systems, and climate services for reducing emissions, that will enable the building of resilient, thriving sustainable cities that promote the Sustainable Development Goals. A number of recent international studies have been initiated to explore these issues. The paper provides a brief overview of recent WMO and collaborators research programs and activities in urban hydrometeorology, climate and air pollution; describes the novel concept of urban integrated weather, climate and environment related services; and highlights research needs for their realisation.