Operational Weather Forecasting Research Articles

Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery

Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal- and high-spatial-resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite 16 (GOES-16) imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery and backtracking them using optical flow to determine if they originated from a deep convection source. This “objective OFB identification” is tested with a case study of an OFB-triggered dust storm over southern Arizona. The results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is the incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.

A deep‐learning model to predict thunderstorms within 400 km2 South Texas domains

AbstractA deep‐learning neural network (DLNN) model was developed to predict thunderstorm occurrence within 400 km2 South Texas domains for up to 15 hr (±2 hr accuracy) in advance. The input features were chosen primarily from numerical weather prediction model output parameters/variables; cloud‐to‐ground lightning served as the target. The deep‐learning technique used was the stacked denoising autoencoder (SDAE) in order to create a higher order representation of the features. Logistic regression was then applied to the SDAE output to train the predictive model. An iterative technique was used to determine the optimal SDAE architecture. The performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models, a classifier via a combination of principal component analysis and logistic regression, and operational weather forecasters, based on the same data set.

The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally

AbstractThe Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis are five operational numerical weather forecasts, three operational analyses, and one reanalysis product. The products have hourly to daily temporal resolution and 2.5–50-km spatial resolution. To date the archive contains 394 TB of data while 368 GB of new data are added every night. The data are archived in CF-1.6-compliant netCDF-4 format. The archive is available online (https://caspar-data.ca) since June 2017 and allows users to precisely request data according to their needs, that is, spatial cropping based on a standard shape or uploaded shapefile of the domain of interest and selection of forecast horizons, variables, and issue dates. The degree of customization in CaSPAr is a unique feature relative to other publicly accessible numerical weather prediction archives and it minimizes user download requirements and local processing time. We benchmark the processing time and required storage of such requests based on 216 test scenarios. We also demonstrate how CaSPAr data can be employed to analyze extreme rainfall events. CaSPAr provides access to data that are fundamental for evaluating numerical weather prediction models and demonstrating the improvement in products such as flood and energy demand forecasting systems.

Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis

Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values.

An evaluation of operational and research weather forecasts for southern West Africa using observations from the DACCIWA field campaign in June–July 2016

AbstractReliable and accurate weather forecasts, particularly those of rainfall and its extremes, have the potential to improve living conditions in densely populated southern West Africa (SWA). The limited availability of observations has long impeded a rigorous evaluation of current state‐of‐the‐art forecast models. The field campaign of the Dynamics‐Aerosol‐Chemistry‐Cloud Interactions in West Africa (DACCIWA) project in June–July 2016 has created an unprecedentedly dense set of measurements from surface stations and radiosondes. Here we present results from a comprehensive evaluation of both numerical model forecasts and satellite products using these data on a regional and local level. Results reveal a substantial observational uncertainty showing considerable underestimations in satellite estimates of rainfall and low‐cloud cover with little correlation at the local scale. Models have a dry bias of 0.1–1.9 in rainfall and too low column relative humidity. They tend to underestimate low clouds, leading to excess surface solar radiation of 43 . Remarkably, most models show some skill in representing regional modulations of rainfall related to synoptic‐scale disturbances, while local variations in rainfall and cloudiness are hardly captured. Slightly better results are found with respect to temperature and for the post‐onset rather than for the pre‐onset period. Delicate local features such as the Maritime Inflow phenomenon are also rather poorly represented, leading to too cool, dry and cloudy conditions at the coast. Differences between forecast days 1 and 2 are relatively small and hardly systematic, suggesting a relatively quick error saturation. Using explicit convection leads to more realistic spatial variability in rainfall, but otherwise no marked improvement. Future work should aim at improving the subtle balance between the diurnal cycles of low clouds, surface radiation, the boundary layer and convection. Further efforts are also needed to improve the observational system beyond field campaign periods.

Validation of high‐impact weather event predictions using an integrated institutional–citizen observational network

AbstractAn integrated institutional–citizen observational database has been created in the Emilia‐Romagna region (Italy) to monitor the occurrence of high‐impact weather events and validate the associated operational weather forecasts. In the following, after reporting the main characteristics of the database, its utility is presented for two extended snowfall events that occurred in January 2019, describing the observed synoptic conditions, the forecast for total precipitation, the forecast bulletins for total snow depth and their validations. The use of the integrated network allows an improved space coverage over the territory with respect to the specific snow parameter, especially in highly populated plain areas not covered by institutional data and likely to be more heavily affected by the presence of snow cover.

Meteorological conditions leading to the 2015 Salgar flash flood: lessons for vulnerable regions in tropical complex terrain

Abstract. Flash floods are a recurrent hazard for many developing Latin American regions due to their complex mountainous terrain and the rainfall characteristics in the tropics. These regions often lack the timely and high-quality information needed to assess, in real time, the threats to the vulnerable communities due to extreme hydrometeorological events. The systematic assessment of past extreme events allows us to improve our prediction capabilities of flash floods. In May 2015, a flash flood in the La Liboriana basin, municipality of Salgar, Colombia, caused more than 100 casualties and significant infrastructure damage. Despite the data scarcity, the climatological aspects, meteorological conditions, and first-order hydrometeorological mechanisms associated with the La Liboriana flash flood, including orographic intensification and the spatial distribution of the rainfall intensity relative to the basin's geomorphological features, are studied using precipitation information obtained using a weather radar quantitative precipitation estimation (QPE) technique, as well as from satellite products, in situ rain gauges from neighboring basins, quantitative precipitation forecasts (QPFs) from an operational Weather Research and Forecasting Model (WRF) application, and data from reanalysis products. The La Liboriana flash flood took place during a period with negative precipitation anomalies over most of the country as a result of an El Niño event. However, during May 2015, moist easterly flow towards the upper part of La Liboriana caused significant orographic rainfall enhancement. The overall evidence shows an important role of successive precipitation events in a relatively short period and of orography in the spatial distribution of rainfall and its intensification as convective cores approached the steepest topography. There were three consecutive events generating significant rainfall within the La Liboriana basin, and no single precipitation event was exceptionally large enough to generate the flash flood, but rather the combined role of precedent rainfall and the extreme hourly precipitation triggered the event. The results point to key lessons for improving local risk reduction strategies in vulnerable regions with complex terrain.

Assessment of Integrated Water Vapor Estimates from the iGMAS and the Brazilian Network GNSS Ground-Based Receivers in Rio de Janeiro

There is pressing demand for knowledge improvement of the integrated water vapor (IWV) distribution in regions affected by heat islands that are associated with extreme rainfall events such as in the metropolitan area of Rio de Janeiro (MARJ). This work assessed the suitability and evaluation of the spatiotemporal distribution of Global Navigation Satellite Systems (GNSS) IWV from the cooperation of the International GNSS Monitoring and Assessment System (iGMAS) and the National Observatory (Observatório Nacional, ON) of Brazil, from the Brazilian Network for Continuous Monitoring (RBMC), and IWV products from Moderate Resolution Imaging Spectroradiometer (MODIS) and radiosonde, jointly with surface meteorological data, in two sectors of the state of Rio de Janeiro from February 2015–August 2018. High variability of the near surface air temperature (T) and relative humidity (RH) were observed among eight meteorological sites. The mean T differences between sites, up to 4.4 °C, led to mean differences as high as 3.1 K for weighted mean temperature (Tm) and hence 0.83 mm for IWV differences. Local grid points of MODIS IWV estimates had relatively good agreement with the GNSS-derived IWV, with mean differences from –2.4–1.1 mm for the daytime passages of the satellites TERRA and AQUA and underestimation from –9 mm to –3 mm during nighttime overpasses. A contrasting behavior was found in the radiosonde IWV estimates compared with the estimates from GNSS. There were dry biases of 1.4 mm (3.7% lower than expected) by radiosonde IWV during the daytime, considering that all other estimates were unbiased and the differences between IWVGNSS and IWVRADS were consistent. Based on the IWV comparisons between radiosonde and GNSS at nighttime, the atmosphere over the radiosonde site is about 1.2 mm and 2.3 mm wetter than that over the RBMC RIOD and iGMAS RDJN stations, respectively. The atmosphere over the site RIOD was 1.2 mm wetter than over that of RDJN for all three-hour means. These results showed that there were important variabilities in the meteorological conditions and in the distribution of water vapor in the MERJ. The data from the iGMAS RDJN station were feasible, together with those from the RBMC, MODIS, and radiosonde data, to investigate IWV in the region with occurrence of heat islands and peculiar physiographic and meteorological characteristics. This work recommends the magnification of the GNSS network in the state of Rio de Janeiro with the use of data from complete meteorological station collocated near every GNSS receiver, aiming to improve local IWV estimates and serving as additional support for operational numerical assimilation, weather forecast, and nowcast of extreme rainfall and flooding events.

Impacts of Assimilating High-Resolution Atmospheric Motion Vectors Derived from Himawari-8 on Tropical Cyclone Forecast in HWRF

AbstractThe impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this degradation: 1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and 2) a drying around and outside the RMW. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast.

A Generative Adversarial Gated Recurrent Unit Model for Precipitation Nowcasting

Precipitation nowcasting is an important task in operational weather forecasts. The key challenge of the task is the radar echo map extrapolation. The problem is mainly solved by an optical-flow method in existing systems. However, the method cannot model rapid and nonlinear movements. Recently, a convolutional gated recurrent unit (ConvGRU) method is developed, which aims to model such movements based on deep learning techniques. Despite the promising performance, ConvGRU tends to yield blurring extrapolation images and fails to multi-modal and skewed intensity distribution. To overcome the limitations, we propose in this letter a generative adversarial ConvGRU (GA-ConvGRU) model. The model is composed of two adversarial learning systems, which are a ConvGRU-based generator and a convolution neural network-based discriminator. The two systems are trained by playing a minimax game. With the adversarial learning scheme, GA-ConvGRU can yield more realistic and more accurate extrapolation. Experiments on real data sets have been conducted and the results demonstrate that the proposed GA-ConvGRU significantly outperforms state-of-the-art extrapolation methods ConvGRU and optical flow.

Assessing Bias Correction Methods in Support of Operational Weather Forecast in Arid Environment

In this study, the Weather Research and Forecasting (WRF) model is employed for operational forecasting over the United Arab Emirates (UAE). The goal of this study is to assess two bias correction methods, namely the multiplicative Ratio Correction (RC) and Kalman Filter (KF), in support of operational mesoscale forecasts in the UAE. These techniques are applied to the 2-m temperature with the corrected temperature subsequently used to update the Relative Humidity (RH) predictions. The simulation covers the 2-year period 1st January 2017 to 31st December 2018. To evaluate the WRF performance, Meteorological Aerodrome Reports (METARs) observations at five airport stations are used. It is concluded that when any of the bias correction techniques are applied, there is a significant reduction of the bias and Root-Mean-Square-Error (RMSE). This is particularly true in the summer season and during nighttime and early morning hours, when WRF has a systematic cold bias of up to 2 °C. In addition, the bias distribution is more symmetric with a reduced spread, skewness and kurtosis values. The RC technique is found to give the best scores, with the observed and modelled temperatures generally within 0.25 °C for the first two forecast days. In addition, it successfully removes the model tendency of underperforming in the warm season. A similar improvement in the skill scores is seen in the RH forecasts albeit with smaller magnitudes. The KF and RC techniques used here have been employed successfully in operational forecasts with the potential to expand them to other model variables.

PREDICTION OF OCCURRENCE OF CLOUD-TO-GROUND ELECTRICAL DISCHARGES USING FORECASTS OF THE BRAMS NUMERICAL MODEL

The prediction of occurrence of severe convective events allows to issue meteorological alerts in order to reduce potential catastrophic events. In many cases, numerical models for weather forecast are not able to efficiently simulate such events. Alternatively, considering the large amount and diversity of meteorological data, the employment of Data Mining techniques has become widespread. In the case of convective activity, the use of past data allows to identify characteristic patterns in the model forecasts by associating them to the corresponding field of density of occurrence of cloud-to-ground atmospheric electrical discharges. Such task is performed by a machine learning algorithm, in this case, a set of neural networks. As a consequence, these patterns can be detected in future forecasts generated by the numerical model, allowing to predict the occurrence of discharges, which are associated to convective activity. In the current work, the proposed approach was applied to BRAMS, a numerical model developed in the country for operational and research use in Meteorology. Consequently, the ability to predict the occurrence of discharges by the set of neural networks was analysed for some selected events as well as its usefulness as an ancillary tool in operational weather forecast.

Performance Optimization of Operational WRF Model Configured for Indian Monsoon Region

Providing timely weather forecasts from operational weather forecast centers is extremely critical as many sectors rely on accurate predictions provided by numerical weather models. Weather research and forecasting (WRF) is one of primary tools used in generating weather predictions at operational weather centers, and an optimal domain configuration of WRF along with suitable combination of robust computational resources and high-speed network are required to achieve the maximum performance on high-performance computing cluster (HPC) for the WRF model to deliver the timely forecasts. In this study, we have analyzed the number of methods to optimize WRF model to reduce computational time taking for the operational weather forecasts tested on HPC available at University Grants Commission center for mesosphere stratosphere troposphere radar applications, Sri Venkateswara University (SVU). To do this exercise, we have prepared a benchmark dataset by configuring WRF model for the Indian monsoon region as similar to real-time weather forecasting system model configuration. We have first carried out a series of scalability tests by increasing the number of computational nodes till it reaches a scalable point using the prepared benchmark dataset. Our node scalability results indicate the WRF model is scalable up to 65 nodes for the benchmark dataset and configured model domain on HPC available at UGC SVU center. As the total time taken for generating the model forecasts is the sum of computational time taken for predicting weather and the input/output (IO) time for writing into the storage disks. Further, we have performed several tests to optimize the time taken for IO by the weather model, and the results of IO tests clearly indicate that the WRF configured with parallel IO is highly beneficial method to reduce the total time taken for the generation of weather forecasts by the WRF model.

New Methods for Data Storage of Model Output from Ensemble Simulations

Abstract Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts.

Summary of the plenary sessions at European Space Weather Week 15: space weather users and service providers working together now and in the future

During European Space Weather Week 15 two plenary sessions were held to review the status of operational space weather forecasting. The first session addressed the topic of working with space weather service providers now and in the future, the user perspective. The second session provided the service perspective, addressing experiences in forecasting development and operations. Presentations in both sessions provided an overview of international efforts on these topics, and panel discussion topics arising in the first session were used as a basis for panel discussion in the second session. Discussion topics included experiences during the September 2017 space weather events, cross domain impacts, timeliness of notifications, and provision of effective user education. Users highlighted that a severe space weather event did not necessarily lead to severe impacts for each individual user across the different sectors. Service providers were generally confident that timely and reliable information could be provided during severe and extreme events, although stressed that more research and funding were required in this relatively new field of operational space weather forecasting, to ensure continuation of capabilities and further development of services, in particular improved forecasting targeting user needs. Here a summary of the sessions is provided followed by a commentary on the current state-of-the-art and potential next steps towards improvement of services.

A Variational Approach to Data Assimilation in the Solar Wind

AbstractVariational data assimilation (DA) has enabled huge improvements in the skill of operational weather forecasting. In this study, we use a simple solar‐wind propagation model to develop the first solar‐wind variational DA scheme. This scheme enables solar‐wind observations far from the Sun, such as at 1 AU, to update and improve the inner‐boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near‐Earth solar wind, even when the observations are not directly downstream of the Earth. Using controlled experiments with synthetic observations, we demonstrate this method's potential to improve solar wind forecasts, though the best results are achieved in conjunction with accurate initial estimates of the solar wind. The variational DA scheme is also applied to Solar‐Terrestrial Relations Observatory (STEREO) in situ observations using initial solar wind conditions supplied by a coronal model of the observed photospheric magnetic field. We consider the period October 2010 to October 2011, when the STEREO spacecraft were approximately 80∘ ahead/behind Earth in its orbit. For 12 of 13 Carrington Rotations, assimilation of STEREO data improves the near‐Earth solar wind estimate over the nonassimilated state, with a 18.4% reduction in the root mean square error. The largest gains are made by the DA during times when the steady‐state assumption of the coronal models breaks down. While applying this pure variational approach to complex solar‐wind models is technically challenging, we discuss hybrid DA approaches which are simpler to implement and may retain many of the advantages demonstrated here.

Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial Systems Modeling Platform (v1.1.0)

Operational weather and flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g., groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over Europe in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.

Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation

Abstract This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.

An Adiabatic Mechanism for the Reduction of Jet Meander Amplitude by Potential Vorticity Filamentation

Abstract The amplitude of ridges in large-amplitude Rossby waves has been shown to decrease systematically with lead time during the first 1–5 days of operational global numerical weather forecasts. These models also exhibit a rapid reduction in the isentropic gradient of potential vorticity (PV) at the tropopause during the first 1–2 days of forecasts. This paper identifies a mechanism linking the reduction in large-scale meander amplitude on jet streams to declining PV gradients. The mechanism proposed is that a smoother isentropic transition of PV across the tropopause leads to excessive PV filamentation on the jet flanks and a more lossy waveguide. The approach taken is to analyze Rossby wave dynamics in a single-layer quasigeostrophic model. Numerical simulations show that the amplitude of a Rossby wave propagating along a narrow but smooth PV front does indeed decay transiently with time. This process is explained in terms of the filamentation of PV from the jet core and associated absorption of wave activity by the critical layers on the jet flanks, and a simple method for quantitatively predicting the magnitude of the amplitude reduction without simulation is presented. Explicitly diffusive simulations are then used to show that the combined impact of diffusion and the adiabatic rearrangement of PV can result in a decay rate of Rossby waves that is 2–4 times as fast as could be expected from diffusion acting alone. This predicted decay rate is sufficient to explain the decay observed in operational weather forecasting models.

Classification of Extreme Cold Incursions over South America

AbstractCold-air incursions across South America present a variety of problems, sometimes by advancing to the Amazon basin and causing agricultural losses related to extreme low-temperature events. The synoptic conditions that produce cold-air incursions are relatively well understood; however, the most affected region depends on the route by which cold air spreads across the continent. Therefore, the classification of extreme cold-air incursions allows a better understanding of the particularities directly related to the aforementioned losses. In this work, similarities and differences among extreme cold surges were found through time series correlation of anomaly temperatures in four selected areas and compositing techniques from ERA-Interim reanalysis datasets, resulting in three distinct patterns: meridional penetration (MP), meridional restriction (MR), and east penetration (EP). The patterns identified here enable a more detailed understanding of the synoptic patterns and forcing mechanisms associated with extreme cold-air incursions and therefore can be used for operational weather forecasting.