Operational Forecasting System Research Articles

Precipitation representation over a two‐year period in regional reanalysis

Precipitation is a critical aspect of climate. In Europe, extreme precipitation events are costly and represent a threat to life. Evidence suggests the frequency and intensity of these events is increasing in Europe and therefore long‐period datasets that can represent such events accurately are required. Precipitation is challenging to represent in gridded models and therefore is not well trusted in relatively low‐resolution global reanalyses. For the first time, the European Reanalyses and Observations For Monitoring (EURO4M) project has produced high‐resolution atmospheric regional reanalyses of Europe that improve representation of precipitation. These are based on operational forecast systems at the Met Office and the Swedish Meteorological and Hydrological Institute (HIRLAM). The improvement of quality of precipitation in the regional reanalysis datasets over their parent global reanalysis is demonstrated here, together with a discussion on the difficulties of validation of gridded precipitation. It is shown that regional reanalyses show particular improvement in representing high‐threshold events. It is also shown that higher resolution, time‐varying data assimilation and direct assimilation of precipitation all contribute to improving representation of precipitation. Resolution is of particular importance when representing extreme events.

Operational Wave Prediction System at Environment Canada: Going Global to Improve Regional Forecast Skill

Abstract A global deterministic wave prediction system (GDWPS) is used to improve regional forecasts of waves off the Canadian coastline and help support the practice of safe marine activities in Canadian waters. The wave model has a grid spacing of ¼° with spectral resolution of 36 frequency bins and 36 directional bins. The wave model is driven with hourly 10-m winds generated by the operational global atmospheric prediction system. Ice conditions are updated every three hours using the ice concentration forecasts generated by the Global Ice–Ocean Prediction System. Wave forecasts are evaluated over two periods from 15 August to 31 October 2014 and from 15 December 2014 to 28 February 2015, as well as over select cases during the fall of 2012. The global system is shown to improve wave forecast skill over regions where forecasts were previously produced using limited-area models only. The usefulness of a global expansion is demonstrated for large swell events affecting the northeast Pacific. The first validation of a Canadian operational wave forecast system in the Arctic is presented. Improvements in the representation of forecast wave fields associated with tropical cyclones are also demonstrated. Finally, the GDWPS is shown to result in gains of at least 12 h of lead time.

SOCIB operational ocean forecasting system and multi-platform validation in the Western Mediterranean Sea

ABSTRACTThis paper describes the high-resolution Western Mediterranean Sea Operational Forecasting System (WMOP) developed at the Balearic Islands Coastal Observing and Forecasting System (SOCIB). The system runs on a daily basis driven by high-resolution atmospheric forcing, providing 3-day forecasts of physical oceanic variables with a 2 km horizontal resolution, thus representing the ocean variability from mesoscale to basin scale from the Gibraltar Strait to the Sardinia Channel. A systematic regional monitoring and validation system has been developed using multi-platform observations, allowing the evaluation of both the overall realism of the predictions and the specific errors in each sub-basin.

Impact of different initialisation methods on the quality of sea forecasts for the Sicily Channel

ABSTRACTAn operational ocean forecasting system provided numerical predictions on the main hydrodynamics in the seas around Sicily. The system is evaluated using an independent dataset of sea surface temperature (SST) satellite observations. A set of specific numerical experiments are carried and the simulated SST data was compared with the satellite data observed at selected dates. Two kinds of initialisation techniques are adopted to generate different initial conditions: ‘slave mode’ and ‘partially active mode’. The accuracy of both approaches is analysed to individuate the optimal nesting strategy. The results show that the partially active mode improves the forecasts, resulting in a model accuracy increase of up to about 30%.

Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics

Abstract Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina [Servicio Meteorológico Nacional (SMN)], but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.

Observation impact analysis methods for storm surge forecasting systems

This paper presents a simple method for estimating the impact of assimilating individual or group of observations on forecast accuracy improvement. This method is derived from the nsemble-based observation impact analysis method of Liu and Kalnay (Q J R Meteorol Soc 134:1327–1335, 2008). The method described here is different in two ways from their method. Firstly, it uses a quadratic function of model-minus-observation residuals as a measure of forecast accuracy, instead of model-minus-analysis. Secondly, it simply makes use of time series of observations and the corresponding model output generated without data assimilation. These time series are usually available in an operational database. Hence, it is simple to implement. It can be used before any data assimilation is implemented. Therefore, it is useful as a design tool of a data assimilation system, namely for selecting which observations to assimilate. The method can also be used as a diagnostic tool, for example, to assess if all observation contributes positively to the accuracy improvement. The method is applicable for systems with stationary error process and fixed observing network. Using twin experiments with a simple one-dimensional advection model, the method is shown to work perfectly in an idealized situation. The method is used to evaluate the observation impact in the operational storm surge forecasting system based on the Dutch Continental Shelf Model version 5 (DCSMv5).

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Solar radiation is a key factor in the Earth’s energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010–2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from −30 to 40 W·m−2, absolute root mean square errors (RMSE) from 25 to 60 W·m−2, relative bias ranging from −11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from −50 to 160 W·m−2, absolute RMSE from 60 to 240 W·m−2, relative bias ranging from −30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.

Challenges of building an operational ocean forecasting system for small island regions: regional to local

ABSTRACTAn ocean circulation forecasting model for the Madeira Archipelago is operational since May 2010. Developing a forecasting system for a small island oceanic region, deprived from in-situ observations, is a challenging task since there are limited ways to validate predictions. Furthermore, model resolution concurrent with insufficient computational power, locally available, are other limiting factors to consider. Regional models combined with the possibility to downscale solutions onto a higher resolution island-scale model is a way to overcome some of such limitations. Nevertheless, generalised regional models must be able to accurately represent the far-field and transport important features such as meddies onto the local systems; while island-scale models must have sufficient grid resolution as well as adequate physics and accurate atmospheric forcing to resolve the near-field phenomena. An island-induced cyclonic eddy event was successfully observed and forecasted with the current approach (regional-local). Generalised single (regional) model initiatives will prove to be insufficient to deal with mesoscale dynamic systems, islands and seamounts are important generators of mesoscale features in the NE Atlantic, with basin scale implications. The forecasting systems of the future should also consider upscaling valid local (island-scale) solutions onto Regional and/or Global models.

Land surface initialization strategy for a global reforecast dataset

A 32‐year global ensemble reforecast dataset has recently been developed at Météo‐France that is approximatively consistent with the operational global ensemble forecast system PEARP. Unlike ECMWF or NCEP, Météo‐France does not possess a reanalysis of its own operational forecast system. Therefore, the initial atmospheric state and boundary conditions of the reforecasts are from the ECMWF ERA‐Interim reanalysis. This article presents a study of the sensitivity of the reforecasts to the method of land‐surface initialization. To this end, two sets of short‐range hindcasts using different land‐surface initialization approaches are compared. The first set is initialized from interpolated ERA‐Interim land‐surface fields based on a transfer function. The second set is initialized from offline simulations of the Météo‐France land‐surface model (SURFEX) driven by the 3‐hourly near‐surface atmospheric fields of the ERA‐Interim reanalysis. Each set is run from 1800 UTC initial conditions and up to +108 h.Because better results overall are found using offline SURFEX simulations, this latter approach was chosen to perform an ensemble reforecast dataset. Then, this ensemble reforecast database will be used to build a climatology of the operational ensemble prediction system of Météo‐France, which will, in turn, help to better estimate systematic forecast errors and, more importantly, improve the forecasting of rare extreme weather events.

The High-Frequency Coastal Radar Network Operated by Puertos del Estado (Spain): Roadmap to a Fully Operational Implementation

In this paper, a detailed description of the roadmap to transform four individual CODAR SeaSonde high-frequency (HF) radars into an integrated HF radar network operated by Puertos del Estado (Madrid, Spain) is provided. To assess the maturation process into a fully operational status, the system must evolve via an implementation of phased approaches, including: homogenization of maintenance practices, quality control checks in real time, standardization of data access, and development of customized visualization tools. Additionally, continuous validation works with independent in situ sensors are required to provide upper bounds on both radial and total radar current measurement accuracies. Consistent correlation coefficients and uncertainty values emerge in the range of 0.31–0.81 and 8–22 cm/s, respectively, for the remotely estimated velocities. Complementarily, a dedicated online website has been developed to operationally monitor radar system health in real time. This automated quality control application analyzes a number of diagnose parameters to obtain estimates of their standard ranges and evaluate radar site performance according to them. Abrupt changes, gradual degradation, and/or failure problems can be easily detected, triggering alerts for troubleshooting. Once a good performance of HF radar systems is ensured, 2-D surface current maps are used for a broad range of practical applications, i.e., search-and-rescue operations, oil spill response, or the rigorous skill assessment of an operational ocean forecasting system such as Iberia–Biscay–Ireland (IBI), implemented within the frame of the Copernicus Marine Environment Monitoring Service.

토양함수지수를 이용한 국가 산사태 예보체계 구축 및 운영

The objective of this study was to describe the background and methodology of the current operational national landslide forecast system in Republic of Korea and analyze its operational results in the past two years. Soil water index and tank model were employed as the national landslide forecast system with a goal of pre-1-hour forecast for landslide likelihood in a national scale. The tank model was parameterized with the past rainfall data inducing landslides in each divided eleven region after considering rainfall and geological characteristics in the whole country. The national landslide forecast system have provided local administrations with the information of predicted warnings calculated with observed and predicted rainfall data by Korea Weather Administration in a hour since 2013, and they issue landslide warnings including landslide watch and alert based on the information. As results of its two-year-operation, 420 and 248 predicted warnings were calculated in 2013 and 2014, respectively. But, 33 of 420 in 2013 and 17 of 248 in 2014 were not finally issued in reality, accounting for 7.9% and 6.9%, respectively. Also, predicted warnings were not calculated for 36 of 72 landslides occurred in 2014 compared to only one out of 212 landslides in 2013. Overall, improvement of input rainfall data accuracy, regular revision of tank model parameters, and relevant adjustment of landslide warning issuing level were suggested to improve the efficiency of the national landslide forecast system.

Evaluation of the MACC operational forecast system – potential and challenges of global near-real-time modelling with respect to reactive gases in the troposphere

Abstract. The Monitoring Atmospheric Composition and Climate (MACC) project represents the European Union's Copernicus Atmosphere Monitoring Service (CAMS) (http://www.copernicus.eu/), which became fully operational during 2015. The global near-real-time MACC model production run for aerosol and reactive gases provides daily analyses and 5-day forecasts of atmospheric composition fields. It is the only assimilation system worldwide that is operational to produce global analyses and forecasts of reactive gases and aerosol fields. We have investigated the ability of the MACC analysis system to simulate tropospheric concentrations of reactive gases covering the period between 2009 and 2012. A validation was performed based on carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) surface observations from the Global Atmosphere Watch (GAW) network, the O3 surface observations from the European Monitoring and Evaluation Programme (EMEP) and, furthermore, NO2 tropospheric columns, as well as CO total columns, derived from satellite sensors. The MACC system proved capable of reproducing reactive gas concentrations with consistent quality; however, with a seasonally dependent bias compared to surface and satellite observations – for northern hemispheric surface O3 mixing ratios, positive biases appear during the warm seasons and negative biases during the cold parts of the year, with monthly modified normalised mean biases (MNMBs) ranging between −30 and 30 % at the surface. Model biases are likely to result from difficulties in the simulation of vertical mixing at night and deficiencies in the model's dry deposition parameterisation. Observed tropospheric columns of NO2 and CO could be reproduced correctly during the warm seasons, but are mostly underestimated by the model during the cold seasons, when anthropogenic emissions are at their highest level, especially over the US, Europe and Asia. Monthly MNMBs of the satellite data evaluation range from values between −110 and 40 % for NO2 and at most −20 % for CO, over the investigated regions. The underestimation is likely to result from a combination of errors concerning the dry deposition parameterisation and certain limitations in the current emission inventories, together with an insufficiently established seasonality in the emissions.

Sensitivity of HF radar-derived surface current self-organizing maps to various processing procedures and mesoscale wind forcing

We performed a number of sensitivity experiments by applying a mapping technique, self-organizing maps (SOM) method, to the surface current data measured by high-frequency (HF) radars in the northern Adriatic and surface winds modelled by two state-of-the-art mesoscale meteorological models, the Aladin (Aire Limitee Adaptation Dynamique Developpement InterNational) and the Weather and Research Forecasting models. Surface current data used for the SOM training were collected during a period in which radar coverage was the highest: between February and November 2008. Different pre-processing techniques, such as removal of tides and low-pass filtering, were applied to the data in order to test the sensitivity of characteristic patterns and the connectivity between different SOM solutions. Topographic error did not exceed 15 %, indicating the applicability of the SOM method to the data. The largest difference has been obtained when comparing SOM patterns originating from unprocessed and low-pass filtered data. Introduction of modelled winds in joint SOM analyses stabilized the solutions, while sensitivity to wind forcing coming from the two different meteorological models was found to be small. Such a low sensitivity is considered to be favourable for creation of an operational ocean forecasting system based on neural networks, HF radar measurements and numerical weather prediction mesoscale models.

Assimilation of the seabird and ship drift data in the north-eastern sea of Japan into an operational ocean nowcast/forecast system.

At the present time, ocean current is being operationally monitored mainly by combined use of numerical ocean nowcast/forecast models and satellite remote sensing data. Improvement in the accuracy of the ocean current nowcast/forecast requires additional measurements with higher spatial and temporal resolution as expected from the current observation network. Here we show feasibility of assimilating high-resolution seabird and ship drift data into an operational ocean forecast system. Data assimilation of geostrophic current contained in the observed drift leads to refinement in the gyre mode events of the Tsugaru warm current in the north-eastern sea of Japan represented by the model. Fitting the observed drift to the model depends on ability of the drift representing geostrophic current compared to that representing directly wind driven components. A preferable horizontal scale of 50 km indicated for the seabird drift data assimilation implies their capability of capturing eddies with smaller horizontal scale than the minimum scale of 100 km resolved by the satellite altimetry. The present study actually demonstrates that transdisciplinary approaches combining bio-/ship- logging and numerical modeling could be effective for enhancement in monitoring the ocean current.

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

AbstractIn this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1–7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short‐term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

On the Use of Global Flood Forecasts and Satellite-Derived Inundation Maps for Flood Monitoring in Data-Sparse Regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools.

Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea

For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-mean-square error (RMSE) ∼7–8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

Impacts of radiance data assimilation on the Beijing 7.21 heavy rainfall

Precipitation simulation during the Beijing heavy rainfall event on 21 July 2012 has been investigated in this study. A control run of the Weather Research and Forecasting (WRF) model can roughly simulate the mesoscale features and spatial heterogeneity of precipitation over North China, but it cannot capture the locations, intensity and timing of maximum rainfall over the Beijing area compared with ground observations. To gain better initial conditions in the performance of the mesoscale model, Advanced Microwave Sounding Unit (AMSU) radiance data assimilation was carried out. The spatial distribution and intensity of precipitation have been significantly improved. In particular, data assimilation of Advanced Microwave Sounding Unit-A only has similar impacts on heavy rainfall simulation as the AMSU data assimilation run. Data assimilation of Advanced Microwave Sounding Unit-B only has improved the spatial distribution of heavy rainfall, but it has little impact on the intensity of rainfall. However, the timing of heavy rainfall in all simulations is about 5 to 6h later than observations. Further analysis shows that the center and strength of the low-pressure system was not well simulated during the early development of heavy rainfall in the model. Results from radiance data assimilation suggest that improvement of the initial conditions plays a positive impact on heavy rainfall in 24h simulation. Operational forecast system including satellite radiance data assimilation may improve heavy rainfall forecasting.

A route to systematic error in forecasts of Rossby waves

Recent work has shown that both the amplitude of upper‐level Rossby waves and the tropopause sharpness decrease with forecast lead time for several days in some operational weather forecast systems. In this contribution, the evolution of error growth in a case‐study of this forecast error type is diagnosed through analysis of operational forecasts and hindcast simulations. Potential vorticity (PV) on the 320 K isentropic surface is used to diagnose Rossby waves. The Rossby‐wave forecast error in the operational ECMWF high‐resolution forecast is shown to be associated with errors in the forecast of a warm conveyor belt (WCB) through trajectory analysis and an error metric for WCB outflows. The WCB forecast error is characterised by an overestimation of WCB amplitude, a location of the WCB outflow regions that is too far to the southeast, and a resulting underestimation of the magnitude of the negative PV anomaly in the outflow. Essentially the same forecast error development also occurred in all members of the ECMWF Ensemble Prediction System and the Met Office MOGREPS‐15, suggesting that in this case model error made an important contribution to the development of forecast error in addition to initial condition error. Exploiting this forecast error robustness, a comparison was performed between the realised flow evolution, proxied by a sequence of short‐range simulations, and a contemporaneous forecast. Both the proxy to the realised flow and the contemporaneous forecast were produced with the Met Office Unified Model enhanced with tracers of diabatic processes modifying potential temperature and PV. Clear differences between proxy and forecast were found in the way potential temperature and PV are modified in the WCB. These results demonstrate that differences in potential temperature and PV modification in the WCB can be responsible for forecast errors in Rossby waves.

Operational Space Weather Services in National Space Science Center of Chinese Academy of Sciences

Operational Space Weather Services in National Space Science Center of Chinese Academy of Sciences