Showing papers on "Nowcasting published in 2013"
TL;DR: The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere as mentioned in this paper.
332 citations
TL;DR: In this article, the feasibility of nowcasting convective activity is examined by using thermodynamic indices derived from the ground-based microwave radiometer (MWR) observations located at a tropical station, Gadanki (13.5°N, 79.2°E).
Abstract: [1] In the present study, the feasibility of nowcasting convective activity is examined by using thermodynamic indices derived from the ground-based microwave radiometer (MWR) observations located at a tropical station, Gadanki (13.5°N, 79.2°E). There is a good comparison between thermodynamic parameters derived from MWR and colocated GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. Using MWR observations, a nowcasting technique was developed with the data of 26 thunderstorm cases observed at Gadanki. The analysis showed that there are sharp changes in some thermodynamic indices, such as the K index, the humidity index, precipitable water content, the stability index, and equivalent potential temperature lapse rates, about 2–4 h before the occurrence of thunderstorm. A superepoch analysis was made to examine the composite temporal variations of the thermodynamic indices associated with the occurrence of thunderstorms. The superepoch analysis revealed that 2–4 h prior to the storm occurrence, appreciable variations in many parameters are observed, suggesting thermodynamic evolution of the boundary layer convective instability. It is further demonstrated that by monitoring these variations it is possible to predict the ensuing thunderstorm activity over the region at least 2 h in advance. The association between the temporal evolution of thermodynamic indices and convective activity has been tested for the independent case of nine thunderstorms. The present results suggest that ground-based MWR observations can be used effectively to predict the occurrence of thunderstorms at least 2 h in advance.
313 citations
TL;DR: This work considers the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations and combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging.
Abstract: We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.
134 citations
TL;DR: It is found that the now cast performance of single models varies considerably over time, in line with the forecasting literature, and pooling of nowcast models provides an overall very stable nowcast performance over time.
Abstract: SUMMARY
This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low-frequency variable with a large number of high-frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.
127 citations
TL;DR: In this article, the authors discuss the presently planned RBSP capabilities for generating and broadcasting near real-time space weather data, discusses the data products, the ground stations collecting the data, and the users/models that will incorporate the data into test-beds for radiation belt nowcasting and forecasting.
Abstract: Following the launch and commissioning of NASA’s Radiation Belt Storm Probes (RBSP) in 2012, space weather data will be generated and broadcast from the spacecraft in near real-time. The RBSP mission targets one part of the space weather chain: the very high energy electrons and ions magnetically trapped within Earth’s radiation belts. The understanding gained by RBSP will enable us to better predict the response of the radiation belts to solar storms in the future, and thereby protect space assets in the near-Earth environment. This chapter details the presently planned RBSP capabilities for generating and broadcasting near real-time space weather data, discusses the data products, the ground stations collecting the data, and the users/models that will incorporate the data into test-beds for radiation belt nowcasting and forecasting.
90 citations
TL;DR: In this article, the authors investigate the role of the well-known monthly diffusion indices produced by the Institute for Supply Management (ISM) in nowcasting current quarter US GDP growth and find evidence that these ISM indices are helpful in improving the nowcasts when new ISM information becomes available at the beginning of the month, ahead of other monthly indicators.
70 citations
TL;DR: In this article, high-resolution volumetric reflectivity measurements from a C-band weather radar are used to study the characteristics of convective storms in Belgium, and the data are processed by the storm tracking system TITAN using a 40-dBZ reflectivity threshold.
Abstract: High-resolution volumetric reflectivity measurements from a C-band weather radar are used to study the characteristics of convective storms in Belgium. After clutter filtering, the data are processed by the storm-tracking system Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) using a 40-dBZ reflectivity threshold. The 10-yr period of 5-min data includes more than 1 million identified storms, mostly organized in clusters. A storm is observed at a given point 6 h yr−1 on average. Regions of slightly higher probability are generally correlated with orographic variations. The probability of at least one storm in the study area is 15%, with a maximum of 35% for July and August. The number of storms, their coverage, and their water mass are limited most of the time. The probability to observe a high number of storms reaches a maximum in June and in the early afternoon in phase with solar heating. The probability of large storm coverage and large water mass is highest in July and ...
59 citations
TL;DR: The authors consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation.
57 citations
18 Mar 2013
TL;DR: In this paper, the authors focus on the forecasting aspect of tornadoes by dealing primarily with the relationship between the tornadic storm and its environment, and provide an overview of current tornado forecasting procedures within the Severe Local Storms (SELS) Unit at the National Severe Storms Forecast Center.
Abstract: Present-day operational tornado forecasting can be thought of in two parts: anticipation of tornadic potential in the storm environment, and recognition of tornadic storms once they develop. The former is a forecasting issue, while the latter is associated with warnings (or so-called nowcasting). This paper focuses on the forecasting aspect of tornadoes1, by dealing primarily with the relationship between the tornadic storm and its environment. We begin with a short history of tornado forecasting and related research in Section 2, while Section 3 provides an overview of current tornado forecasting procedures within the Severe Local Storms (SELS) Unit at the National Severe Storms Forecast Center (NSSFC). Section 4 gives a short summary of 35 years of SELS tornado and severe thunderstorm forecast verification, while Section 5 describes our current understanding of the connection between tornadoes and their environment. We conclude in Section 6 with our thoughts about the future of tornado forecasting.
55 citations
TL;DR: In this paper, Wang and Sheeley used a combination of statistical time series prediction techniques operating on the output of physically based models, driven by remote sensing data, may offer the first capability of predicting magnetic storms a few days in advance.
Abstract: Today a wide variety of techniques are available for nowcasting and forecasting magnetic storm activity. A brief review of linear time series prediction techniques, with examples, is used to lay a foundation for the description of newer non-linear techniques based on state-space reconstruction. We illustrate the state-space prediction technique in application to predict Dst from ISEE-3 solar wind data. Upstream solar wind data, such as from ISEE-3 or WIND close to the L 1 libration point, provide a prediction lead time of 0.5-1.5 hours. To go beyond the L 1 prediction lead time some information about the solar wind between the L 1 point and the Sun is required. Remote sensing is the measurement of something from a distance, like solar magnetograms or X-ray images. Both empirical and physically based models, driven by remote sensing data, promise a way to make forecasts a few days into the future. A combination of the statistical time series prediction techniques operating on the output of physically based models, driven by remote sensing data, may offer the first capability of predicting magnetic storms a few days in advance. We illustrate this combination of techniques using the output of a potential field model [Wang and Sheeley, 1988] as input to a linear prediction filter to forecast the planetary geomagnetic index. Finally, practical forecasting requires verification. We describe some of the standard measures of forecast performance: skill score, prediction efficiency, and correlation coefficient. The value of cross validation testing is emphasized.
50 citations
TL;DR: In this paper, an object-based approach for comparing convection-permitting model simulations to radar observations using an innovative objectbased approach is presented. But the method is applied to simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) using the Weather Research and Forecasting Model, and compared to data from a ground-based radar.
Abstract: This study presents a method for comparing convection-permitting model simulations to radar observations using an innovative object-based approach. The method uses the automated cell-tracking algorithm, Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN), to identify individual convective cells and determine their properties. Cell properties are identified in the same way for model and radar data, facilitating comparison of their statistical distributions. The method is applied to simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) using the Weather Research and Forecasting Model, and compared to data from a ground-based radar. Simulations with different microphysics and model resolution are also conducted. Among other things, the comparisons between the model and the radar elucidate model errors in the depth and size of convective cells. On average, simulated convective cells reached higher altitudes than the observations. Als...
TL;DR: In this paper, the impact of these humidity and wind observations in a very short-range regional forecast model is assessed over a four-month summer period and a six-week winter period.
Abstract: Wind, humidity and temperature observations from aircraft and radiosondes are generally used to find the best initial state of the atmosphere for numerical weather prediction (NWP). To be of use for very-short-range numerical weather forecasting (or numerical nowcasting), these observations need to be available within several minutes after observation time. Radiosondes have a typically observation latency of over 30 min and arrive too late for numerical nowcasting. Zenith Total Delay (ZTD) observations obtained from a ground-based network of Global Navigation Satellite System (GNSS) receivers can fill this gap of lacking rapid humidity information. ZTD contains information on the total amount of water vapour. Other rapidly available observations, such as radial wind estimates from Doppler weather radars, can also be exploited. Both observations are available with a delay of less than 5 min with adequate spatial resolution. In this article, the impact of assimilation of these humidity and wind observations in a very-short-range regional forecast model is assessed over a four-month summer period and a six-week winter period. As a reference for the impact, GNSS observations are also assimilated in a three-hourly NWP scheme with longer observation cut-off times. The quality of the forecasts is evaluated against radiosonde observations, radar radial wind and hourly precipitation observations. Assimilation of both GNSS ZTD and radar radial winds resulted in a positive impact on humidity, rainfall and wind forecasts.
TL;DR: In this paper, the authors developed data driven models for nowcasting and forecasting turbidity and chlorophyll-a using Artificial Neural Network (ANN) combined with Genetic Algorithm (GA).
TL;DR: It is concluded that, when Google Trends variables are significantly different from zero in-sample, they tend to lead to improvements in out-of-sample predictive ability.
Abstract: This work presents a study describing the use of Internet search information to achieve an improved nowcasting ability with simple autoregressive models, using data from four countries and two different application domains with social and economic significance: unemployment rate and car sales. The results we obtained differ by country/language and application area. In the case of unemployment, we find that Google Trends data lead to the improvement of nowcasts in three out of the four considered countries: Portugal, France and Italy. However, there are sometimes important differences in the predictive ability of these data when we consider different out-of-sample periods. For car sales, we find that, in some cases, the volume of search queries helps explaining the variance of the car sales data. However, we find little support for the hypothesis that search query data may improve predictions, and we present several possible reasons for these results. Taking all results into account, we conclude that, when Google Trends variables are significantly different from zero in-sample, they tend to lead to improvements in out-of-sample predictive ability. The results can have implications for nowcasting, by providing some indications regarding the advantage or not of the use of search data to improve simple models and indirectly by highlighting the sensitivity of the approach to the actual country-specific base, nowcasting period and search data.
TL;DR: In this article, the authors adopt ensemble techniques in a 4-year nowcast experiment for two nested flash-flood-prone basins in the southern Swiss Alps to quantify the uncertainties in the weather radar quantitative precipitation estimates (QPE) were accounted for by applying an ensemble of 25 radar fields.
Abstract: The quality of hydrological discharge simulations depends to a great extent on the uncertainties in the meteorological input and the model parameterization. To quantify these uncertainties, we adopt ensemble techniques in a 4-year nowcast experiment for two nested flash-flood–prone basins in the southern Swiss Alps.
The spatiotemporal uncertainties in the weather radar quantitative precipitation estimates (QPE) were accounted for by applying an ensemble of 25 radar fields. To account for uncertainties in model parameterization, a Monte Carlo experiment was run to find 26 equifinal model realizations. The resulting parameter ensemble, consisting of 26 members, was run with precipitation input obtained from interpolated pluviometer data and with the deterministic operational weather radar QPE.
To produce the discharge nowcast, the PREcipitation-Runoff-EVApotranspiration HRU Model (PREVAH) was used. PREVAH was calibrated for the main catchment Verzasca. The results for the sub-catchment Pincascia are an independent internal verification of the nowcasting system.
The three ensemble nowcasts and the two deterministic nowcasts are evaluated for a 4-year time series and for two events included in that period. The event analysis shows no clear superiority for either pluviometer-based or radar-based nowcasts. The performance for single events depends heavily on the storm characteristics. However, the evaluation of the 4-year nowcast shows that pluviometer-based nowcasts outperform radar-based nowcasts in the gauged and calibrated catchment and that there is added value in the application of parameter ensembles.
For the small, ungauged catchment, the results achieved by the radar-based nowcasts are superior to the pluviometer-based nowcasts. Especially the radar ensemble proves to be of significant advantage for flash flood nowcasts in such catchments. Copyright © 2012 John Wiley & Sons, Ltd.
TL;DR: In this article, two radar-based ensemble forecasting chains for flash-flood early warn- ing are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments.
Abstract: This study explores the limits of radar-based fore- casting for hydrological runoff prediction. Two novel radar- based ensemble forecasting chains for flash-flood early warn- ing are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Oro- graphic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble fore- casting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 differ- ent initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forc- ing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL- C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rain- fall forecasts (NORA) initialised form a single initial con- dition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic pre- cipitation.
TL;DR: SPoRT will draw on new instrumentation from satellites such as the Soil Moisture Active Passive (SMAP), which will provide high spatial resolution soil moisture data for diagnostic studies and data assimilation and weather forecasting, and the Global Precipitation Mapping (GPM) mission for more accurate measurements of precipitation at fine space and time scales.
Abstract: Established in 2002 to demonstrate the weather forecasting application of real-time EOS measurements, the SPoRT project has grown to be an end-to-end research to operations activity focused on the use of advanced modeling and data assimilation techniques, nowcasting tools, and unique high-resolution multispectral observational data from NASA, NOAA, DoD, and international partner satellites to improve short-term weather forecasts on a regional and local scale. Through these efforts, SPoRT strives to be a focal point and facilitator for the transfer of unique Earth science technologies to the operational weather community with an emphasis on short-term forecasting. To achieve this vision, the SPoRT project will continue to address new data and technologies and develop and test solutions to critical forecast problems, and integrate solutions into end user decision support tools. SPoRT will draw on new instrumentation from satellites such as the Soil Moisture Active Passive (SMAP), which will provide high spatial resolution soil moisture data for diagnostic studies and data assimilation and weather forecasting, and the Global Precipitation Mapping (GPM) mission for more accurate measurements of precipitation at fine space and time scales.
TL;DR: This study compares different ways to use and combine satellite precipitation estimates and numerical weather prediction model fields over the South African domain in order to determine the optimal estimate of precipitation, which can be applied in real-time to support flash flood guidance.
Abstract: The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of precipitation data are crucial to support hydrometeorological warning services. Satellite rainfall estimation provides a very important data source for flash flood guidance systems as well as nowcasting of precipitation events for the data sparse regions of the African continent. Although low earth orbiting satellites with microwave instruments provide good quality rainfall estimates, their temporal and spatial resolution are not adequate for time-critical services. Precipitation estimation using geostationary satellites is less accurate, but provides excellent spatial coverage, is updated frequently and is available in real-time. This study compares different ways to use and combine satellite precipitation estimates and numerical weather prediction model fields over the South African domain in order to determine the optimal estimate of precipitation, which can also be applied in real-time to support flash flood guidance.
TL;DR: In this paper, an object-based method, named PERCAST (PERsiann-Forecast), was proposed to identify, track, and nowcast storms, which predicts the location and rate of rainfall up to 4h using the most recent storm images to extract storm features, such as advection field and changes in storm intensity and size.
TL;DR: In this paper, the effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses.
Abstract: Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lea...
Abstract: . In this paper, recent changes to the Meteosat thunderstorm TRacking And Monitoring algorithm (Cb-TRAM) are presented as well as a validation of Cb-TRAM against data from the European ground-based LIghtning NETwork (LINET) of Nowcast GmbH and the South African Weather Service Lightning Detection Network (SAWS LDN). Validation is conducted along the well-known skill measures probability of detection (POD) and false alarm ratio (FAR) on the basis of Meteosat/SEVIRI pixels as well as on the basis of thunderstorm objects. The values obtained demonstrate specific limitations of Cb-TRAM, as well as limitations of satellite methods in general which are based on thermal emission and solar reflectivity information from thunderstorm cloud tops. Although the climatic conditions and the occurrence of thunderstorms are quite different for Europe and South Africa, quality score values are similar. Our conclusion is that Cb-TRAM provides robust results of well-defined quality for very different climatic regimes. The POD for a thunderstorm with intense lightning is about 80% during the day. The FAR for a Cb-TRAM detection which is not even close to intense lightning is about 50%. If only proximity to any lightning activity is required, FAR is much lower at about 15%. Pixel-based analysis shows that detected thunderstorm object size is not indiscriminately large, but well within physical limitations of the satellite method. Night-time POD and FAR are somewhat worse as the detection scheme does not use the high-resolution visible information during night-time hours. Nowcasting scores show useful values up to approximately 30 min in advance.
TL;DR: In this article, a new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed to analyze movements of radar echoes at different spatial scales.
Abstract: A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation (TREC) technique with a large “box” to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small “box” to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies: the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scale tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.
TL;DR: In this paper, the authors used the databases obtained by observer networks and the databases of C-band and S-band radar to build an algorithm to estimate the vertical component of kinetic energy produced by a hail precipitation.
TL;DR: In this paper, various ARIMA (Autoregressive Integrated Moving Average) models for SSN nowcasting are inferred and discussed and the forecasting accuracy is studied as a function of season, of procedure used to obtain a binary time series and of the type of white noise distribution, respectively.
07 Oct 2013
TL;DR: In this article, the authors presented the first results of the newly developed physically-based and spatially distributed snow cover model SNOWGRID, which is driven with gridded meteorological input data of the in- tegrated nowcasting model INCA (8.1 - 17.7°E; 45.8 - 49.5°N) that uses remote sensing and radar data as well as ground observations.
Abstract: We present first results of the newly developed, physically-based and spatially distributed snow cover model SNOWGRID. The model is driven with gridded meteorological input data of the in- tegrated nowcasting model INCA (8.1 - 17.7°E; 45.8 - 49.5°N) that uses remote sensing and radar data as well as ground observations and is operated by the Austrian weather service ZAMG. Addition- al data from remote sensing and ground measurements are used to validate and calibrate the model output consisting mainly of snow height and snow water equivalent maps in a spatial resolution of 100 m and a time resolution of 15 minutes in near real-time. Its energy balance mode contains partly newly developed schemes (e.g. radiation, cloudiness) based on high quality solar and terrestrial radiation data, satellite products and ground measurements. Snow physical properties and snow cover dynam- ics are currently incorporated in the model based on a simple 2-layer scheme, as the primary focus of the model are fast calculations on the large grid and to accurately represent the spatial distribution of the snow mass and depth (and not its detailed microstructural behavior), which is of great interest for authorities and the general public. Snow extent from SNOWGRID together with satellite data is also used to evaluate the effect of initializing a numerical weather prediction model such as AROME using a real snow distribution instead of climatological estimates as it is operationally done. As the model is still in development, the results and methods shown here are preliminary and not complete yet.
TL;DR: In this paper, an attempt to combine rainfall prediction from a high-resolution mesoscale weather model and a radar-based rainfall model was performed, and the results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 to TM andWRF, respectively.
Abstract: The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar-based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high-resolution mesoscale weather model and a radar-based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.
TL;DR: Wang et al. as mentioned in this paper presented an analysis of lightning forecasting based on atmospheric electrostatic field (EF), radar and lightning location data in Nanjing of China. But they did not consider the effect of cloud-ground (CG) lightning nowcasting.
TL;DR: In this paper, the authors introduce the Toll Index, a new monthly indicator for business cycle forecasting, and demonstrate its relevance using German data, which measures the monthly transportation activity performed by heavy transport vehicles across the country and has highly desirable availability properties.
Abstract: Nowcasting has been a challenge in the recent economic crisis. We introduce the Toll Index, a new monthly indicator for business cycle forecasting, and demonstrate its relevance using German data. The index measures the monthly transportation activity performed by heavy transport vehicles across the country and has highly desirable availability properties (insignificant revisions, short publication lags) as a result of the innovative technology underlying its data collection. It is coincident with production activity due to the prevalence of just-in-time delivery. The Toll Index is a good early indicator of production as measured, for instance, by the German Production Index, provided by the German Statistical Office, which is a well-known leading indicator of the gross national product. The proposed new index is an excellent example of technological, innovation-driven economic telemetry, which we suggest should be established more around the world. Copyright © 2011 John Wiley & Sons, Ltd.
TL;DR: In this paper, the time series of precipitable water (PW) in 30min intervals has been determined through experimentation and operational application of a ground-based global positioning system (GPS) network in Chengdu Plain, which is used for precise and reliable meteorological research.
Abstract: The time series of precipitable water (PW) in 30 min intervals has been determined through experimentation and operational application of a ground-based global positioning system (GPS) network in Chengdu Plain, which is used for precise and reliable meteorological research. This study is the first to apply PW to the southwest vortex (SWV) and heavy rain events by using the data from an intensive SWV experiment conducted in summer 2010. The PW derived from the local ground-based GPS network was used in the monitoring and analysis of heavy rain caused by the SWV and the Tibetan Plateau vortex (TPV). Results indicate that an increase in GPS precipitable water (GPS-PW) occurs prior to the development of the TPV and SWV; rainfall occurs mainly during high levels of GPS-PW. The evolution features of GPS-PW in rainfall process caused by different weather systems over the Tibetan Plateau (TP) also differ. These results indicate the reference values for operational applications of GPS-PW data in short-term forecasting and nowcasting of high-impact weather in addition to further investigation of heavy rain caused by the TPV, SWV, and other severe weather systems over the TP.
TL;DR: The Swarm mission as discussed by the authors is equipped with several instruments which will observe electromagnetic and atmospheric parameters of the near Earth space environment, including magnetospheric ring current, polar maps of ionospheric conductance and plasma convection, indicators of energy deposition like Poynting flux, and prediction of post sunset equatorial plasma irregularities.
Abstract: Sophisticated space weather monitoring aims at nowcasting and predicting solar-terrestrial interactions because their effects on the ionosphere and upper atmosphere may seriously impact advanced technology. Operating alert infrastructures rely heavily on ground-based measurements and satellite observations of the solar and interplanetary conditions. New opportunities lie in the implementation of in-situ observations of the ionosphere and upper atmosphere onboard low Earth orbiting (LEO) satellites. The multi-satellite mission Swarm is equipped with several instruments which will observe electromagnetic and atmospheric parameters of the near Earth space environment. Taking advantage of the multi-disciplinary measurements and the mission constellation different Swarm products have been defined or demonstrate great potential for further development of novel space weather products. Examples are satellite based magnetic indices monitoring effects of the magnetospheric ring current or the polar electrojet, polar maps of ionospheric conductance and plasma convection, indicators of energy deposition like Poynting flux, or the prediction of post sunset equatorial plasma irregularities. Providing these products in timely manner will add significant value in monitoring present space weather and helping to predict the evolution of several magnetic and ionospheric events. Swarm will be a demonstrator mission for the valuable application of LEO satellite observations for space weather monitoring tools.