Rain Forecasts Research Articles

A hybrid approach to improving the skills of seasonal climate outlook at the regional scale

A hybrid seasonal forecasting approach was generated by the National Centers for Environmental Prediction operational Climate Forecast System (CFS) and its nesting Climate extension of Weather Research and Forecasting (CWRF) model to improve forecasting skill over the United States. Skills for the three summers of 2011–2013 were evaluated regarding location, timing, magnitude, and frequency. Higher spatial pattern correlation coefficients showed that the hybrid approach substantially improved summer mean precipitation and 2-m temperature geographical distributions compared with the results of the CFS and CWRF models. The area mean temporal correlation coefficients demonstrated that the hybrid approach also consistently improved the timing prediction skills for both variables. In general, the smaller root mean square errors indicated that the hybrid approach reduced the magnitude of the biases for both precipitation and temperature. The greatest improvements were achieved when the individual models had similar skills. The comparison with a North American multi-model ensemble further proved the feasibility of improving real-time seasonal forecast skill by using the hybrid approach, especially for heavy rain forecasting. Based on the complementary advantages of CFS the global model and CWRF the nesting regional model, the hybrid approach showed a substantial enhancement over CFS real-time forecasts during the summer. Future works are needed for further improving the quality of the hybrid approach through CWRF’s optimized physics ensemble, which has been proven to be feasible and reliable.

Analysis of the mixing processes in the subtropical Advancetown Lake, Australia

Summary This paper presents an extensive investigation of the mixing processes occurring in the subtropical monomictic Advancetown Lake, which is the main water body supplying the Gold Coast City in Australia. Meteorological, chemical and physical data were collected from weather stations, laboratory analysis of grab samples and an in-situ Vertical Profiling System (VPS), for the period 2008–2012. This comprehensive, high frequency dataset was utilised to develop a one-dimensional model of the vertical transport and mixing processes occurring along the water column. Multivariate analysis revealed that air temperature and rain forecasts enabled a reliable prediction of the strength of the lake stratification. Vertical diffusion is the main process driving vertical mixing, particularly during winter circulation. However, a high reservoir volume and warm winters can limit the degree of winter mixing, causing only partial circulation to occur, as was the case in 2013. This research study provides a comprehensive approach for understanding and predicting mixing processes for similar lakes, whenever high-frequency data are available from VPS or other autonomous water monitoring systems.

Heavy Rains at Rio de Janeiro: Risk Monitoring

After the disaster in 2010, in which 67 people died, all victims of hillside sliding in Rio de Janeiro, the Geo Rio Foundation - office from the City Hall dedicated to questions related to hillsides - has elaborated a geologic-geotechnical mapping of risk in 196 communities (slums) at the city and there were identified areas of high, medium and low risk. Based on this research, an Alarm System of Sirens, together with a Monitoring System of Rain (pluviometers), was installed in 103 communities. At the Rio Operations Center a team of meteorologists work 24 hours a day, 7 days a week, doing rain forecast and monitoring. A Meteorological Radar reaching 250km makes the forecast and 83 pluviometers installed at the communities monitor the rain. If the rain reaches a critical level, what can cause accidents, the sirens are activated to warn the population about the risk of sliding and residents are oriented to move to safer places. The Municipal Civil Defense has identified places which can temporarily shelter these residents in the period of heavy rains. Therefore, indoor courts, residents’ associations, churches, schools and other establishments located at or near the communities, are the support points for receiving these residents. Several Simulated Evacuation Exercises have been organized to train and adapt, in a concrete way and as close to reality as possible, the residents of the areas at risk for their evacuation in an emergency situation. These trainings are also a great opportunity of exercising and evaluating the work of the people involved in the activation of sirens and opening of the support points, as well as stimulating the participation of volunteers and increasing the risk perception of the residents. This way, and complementing with integrated, fast and efficient actions of answer and reconstruction, the Municipality of Rio de Janeiro can become a Resilient City, to the heavy rains and to the disasters, and therewith, gain capacity to face, adapt and absorb the impacts of these types of occurrences and restore the normality as soon as it is possible.

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Experiments with two ensemble systems of resolutions 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS) was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighbourhood in FSS to the time and ensemble dimension. The extension was carried out by recognising that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighbourhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems.The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called the ensemble FSS, which is a good representative of FSS for ensemble forecast in comparison with the FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rains, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells.

Assessing the impact of pre‐GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system

AbstractThe forthcoming Global Precipitation Measurement (GPM) Mission will provide next‐generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud‐resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system.We present a data impact study on the assimilation of precipitation‐affected microwave (MW) radiances from a pre‐GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF‐EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single‐observation assimilation experiments. An empirical bias correction for precipitation‐affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case‐study on a convective storm also reveals that the accuracy of ensemble‐based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

The Research Status of Acid Rain

This paper not only makes a detailed introduction about the research of acid rain including the mechanism of formation, the factor of formation, the harm of acid rain and the analysis, detection and forecast of acid rain, but also points out the problem in the research and the control measure of acid rain.

A unified verification system for operational models from Regional Meteorological Centres of China Meteorological Administration

ABSTRACTA new unified verification system was set up for evaluating operational models from Regional Meteorological Centres (RMC) of the China Meteorological Administration (CMA) using traditional verification methods. The functionality of the system includes data transmission and processing, forecast verification, new product validation and operational monitoring. Eight limited‐area models from different regions of China were verified in this study, including Weather Research and Forecasting (WRF) models from four RMCs. Global/Regional Assimilation and Prediction System (GRAPES) models from two RMCs, Mesoscale Model version 5 (MM5) and Advanced Regional Eta‐co‐ordinate Model (AREM) from the remaining two RMCs. Real time ground observations and model analysis are used as truth for surface and upper‐air variables, respectively. The variables verified are 500 hPa geopotential height, wind and temperature at 850 hPa, wind at 250 hPa, 24 and 3 h accumulated precipitation, and 2 m temperature. The results of the RMCs' models were also compared with those from a limited‐area version of the GRAPES model (GRAPES‐MESO) run operationally at the Numerical Weather Prediction Center (NWPC)/CMA. A new score called ‘relative improvement ratio’ was defined for continuous variables in the comparison. The preliminary results for the year 2011 showed that forecast error varied from one RMC to another, e.g., performance is more stable with relatively lower error for the regional centres of Shanghai, Beijing, Guangzhou and Shenyang than others. These centres provide also more accurate predictions of the rain‐shine category and of precipitation amount, especially the moderate rain forecast in 3 h accumulation. Copyright © 2013 Royal Meteorological Society

Analysis and numerical study of a hybrid BGM-3DVAR data assimilation scheme using satellite radiance data for heavy rain forecasts

A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm waterlogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode (BGM)- three-dimensional variational (3DVAR) Data Assimilation (DA) scheme is designed on running the Advanced Research WRF (ARW WRF) model using the Advanced Microwave Sounder Unit A (AMSU-A) satellite radiance data. Results show that: the BGM ensemble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM- 3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clusters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.

Improvement of the surface pressure operator in GRAPES and its application in precipitation forecasting in South China

In this study we investigated the problems involved in assimilating surface pressure in the current global and regional assimilation and prediction system, GRAPES. A new scheme of assimilating surface pressure was proposed, including a new interpolation scheme and a refreshed background covariance. The new scheme takes account of the differences between station elevation and model topography, and it especially deals with stations located at elevations below that of the first model level. Contrast experiments were conducted using both the original and the new assimilation schemes. The influence of the new interpolation scheme and the updated background covariance were investigated. Our results show that the new interpolation scheme utilized more observations and improved the quality of the mass analysis. The background covariance was refreshed using statistics resulting from the technique proposed by Parrish and Derber in 1992. Experiments show that the updated vertical covariance may have a positive influence on the analysis at higher levels of the atmosphere when assimilating surface pressure. This influence may be more significant if the quality of the background field at high levels is poor. A series of assimilation experiments were performed to test the validity of the new scheme. The corresponding simulation experiments were conducted using the analysis of both schemes as initial conditions. The results indicated that the new scheme leads to better forecasting of sea level pressure and precipitation in South China, especially the forecast of moderate and heavy rain.

Indigenous knowledge of seasonal weather forecasting: A case study in six regions of Uganda

Indigenous knowledge of seasonal weather fore- casting could be useful in decision making at village level to best exploit the seasonal distri- bution of rainfall in order to increase or stabilize crop yields. We examined existing indigenous knowledge by interviewing 192 households in six regions of Uganda. Twenty one distinctive indicators were mentioned by local communities for forecasting the start of the dry season, but only few of these indicators were more consis- tently and frequently used in the different dis- tricts. These included the appearance of bush crickets (Ruspolia baileyi Otte), winds blowing from the east to the west, the appearance and movement of migratory birds such as cattle egrets (Bubulcus ibis Linnaeus), and calling by the Bateleur eagle (Terathopius ecaudatus Lesson). For prediction of the start of the rainy season, 22 indicators were mentioned and these included winds blowing from the west to the east, cuckoo birds (Cuculiformes: Cuculidae) start to call, and winged African termite (Coptotermes formosanus Shiraki) swarms leave their nests. Predictors of rain in the following days included presence of red clouds in the morning. Together with the me- teorological forecasts, traditional indicators could be very useful in rain forecasting and improving the timing of agricultural activities.

A multi-model approach for long-term runoff modeling using rainfall forecasts

This work presents the development of a rainfall–runoff model for the Iguaçu River basin in the south of Brazil. The model was developed to support the operational planning of hydroelectric power plants and is intended to compute natural flow predictions based on meteorological rain forecasts. A recurrent fuzzy system model was employed, with parameters estimated by a genetic algorithm using observed rainfall as input. This work presents the recurrent fuzzy model within a multi-model approach, where the input data are furnished as an envelope, resulting in a prediction envelope that has demonstrated the ability to produce robust results.

Ensemble Tropical Rainfall Potential (eTRaP) Forecasts

Abstract Ensemble tropical rainfall potential (eTRaP) has been developed to improve short-range forecasts of heavy rainfall in tropical cyclones. Evolving from the tropical rainfall potential (TRaP), a 24-h rain forecast based on estimated rain rates from microwave sensors aboard polar-orbiting satellites, eTRaP combines all single-pass TRaPs generated within ±3 h of 0000, 0600, 1200, and 1800 UTC to form a simple ensemble. This approach addresses uncertainties in satellite-derived rain rates and spatial rain structures by using estimates from different sensors observing the cyclone at different times. Quantitative precipitation forecasts (QPFs) are produced from the ensemble mean field using a probability matching approach to recalibrate the rain-rate distribution against the ensemble members (e.g., input TRaP forecasts) themselves. ETRaPs also provide probabilistic forecasts of heavy rain, which are potentially of enormous benefit to decision makers. Verification of eTRaP forecasts for 16 Atlantic hurricanes making landfall in the United States between 2004 and 2008 shows that the eTRaP rain amounts are more accurate than single-sensor TRaPs. The probabilistic forecasts have useful skill, but the probabilities should be interpreted within a spatial context. A novel concept of a “radius of uncertainty” compensates for the influence of location error in the probability forecasts. The eTRaPs are produced in near–real time for all named tropical storms and cyclones around the globe. They can be viewed online (http://www.ssd.noaa.gov/PS/TROP/etrap.html) and are available in digital form to users.

Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin

This work presents the development of a rainfall-runoff model for the Iguacu River basin in southern Brazil. The model was developed to support the operation planning of hydroelectric power plants and is intended to predict the natural flow based on meteorological rain forecasts. A recurrent fuzzy system model was employed with parameters estimated by a genetic algorithm using observed rainfall as input. The model performs well using observed rainfall as input; however, its performance using predicted rainfall as input decays with the forecasting horizon, illustrating the effect of meteorological prediction errors. The prototype implementing the model has been used for dispatch planning by the Brazilian Electric System Operator.

Is it worth using short-term weather forecasts for irrigation management?

Increasing efficiency of irrigation may help to increase agricultural production without increasing the amount of irrigation water used. Better use of the rainfall in irrigation management strategy is one possibility, by using rain forecast and thus saving irrigation water. We developed specific anticipatory behaviour rules in the biodecisional model MODERATO to test this assumption. Simulations were done with different forecasting windows, amounts of expected rainfall and waiting days. Results show that the benefits (in term of margin) of using such anticipatory behaviour depend on the irrigation flow rate available, but are almost negligible. Due to this small benefit, a disruption of the irrigation management plan to account for the weather forecast does not seem worthwhile.

Prediction of lightning flash density with the WRF model

Abstract. The Lightning Potential Index (LPI) is a measure of the potential for charge generation and separation that leads to lightning flashes in convective thunderstorms. It is calculated from model simulated updraft and microphysical fields. It was designed to predict the potential of lightning occurrence in operational weather forecasting models, but could possibly be used to improve short-range forecasts of heavy rain. The index is modified here to be model grid-scale transparent between 1 and 4 km (the approximate upper limit of explicit microphysical weather forecasts). Two case studies show that the modification appears to work quite well, and that LPI can be calculated on both an extremely high resolution research-grid (i.e., 1.33 km) and high resolution (i.e., 4 km) operationally compatible forecast grid. Analytical expressions are presented to use the LPI to predict the hourly lightning flash density.

Improving multimodel weather forecast of monsoon rain over China using FSU superensemble

In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are: ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.

Use of a Dense Rain-gauge Network over India for Improving Blended TRMM Products and Downscaled Weather Models

This study makes use of a network of nearly 2100 rain gauges over India in order to statistically modify the Tropical Rainfall Measuring Mission (TRMM) algorithm 3B42. The validation and usefulness of the modified product is determined against rain gauge datasets and from the training and forecast phase of the Florida State University (FSU) multimodel superensemble. We use downscaled member model forecasts to construct superensemble forecasts. The member model forecasts are scaled down using the modified high-resolution TRMM rains during the training phase of the multimodel superensemble. We demonstrate that a mesoscale superensemble thus constructed has forecast efficiencies superior to those of all of the member models and to the ensemble mean (ENSM) and bias-corrected ensemble mean (BcorENSM). The forecast procedure includes a high-resolution downscaling of the model forecast rain and the construction of a multimodel superensemble. This procedure clearly provides a much-improved forecast of rain using metrics such as equitable threat scores (ETSs), anomaly correlations, root mean square (RMS) errors, and their bias scores. We also demonstrate that the performance of this modified TRMM algorithm provides results very clearly comparable to those obtained from the direct use of rain gauges throughout India and the TRMM 3B42 covering a domain of 50°S to 50°N.

Deterministic and fuzzy verification methods for a hierarchy of numerical models

AbstractDeterministic and fuzzy verification methods are compared to assess the Quantitative Precipitation Forecasts (QPF) performance of an hierarchy of models run at Météo‐France: two operational models (ARPEGE and ALADIN) and a prototype version of the high resolution model AROME. The reference data are 24‐h accumulated rainfall values measured by the French climatological network of rain gauges. The deterministic forecasts are converted to frequencies of threshold exceedence in a neighbourhood in order to apply a fuzzy verification method, able to determine the influence of the double penalty. Local and regional versions of the Brier skill score (BSS) are computed and persistence is the selected reference system. An optimal size of the neighbourhood can be determined from the local version of the BSS. The regional version of the BSS increases with the size of the neighbourhood and provides useful information on the reduction of the error for large scales. All the scores show that the ALADIN 3DVAR implementation improves the quality of the ALADIN QPF in comparison to the ARPEGE QPF. Deterministic verification methods show that AROME improves the QPF for weak precipitation but produces too many false alarms for heavy rain. Moreover, the fuzzy approach removes more substantially the double penalty for the heavy rain forecasts of AROME than for the light precipitation of ALADIN. After recalibration the ALADIN QPF have better deterministic scores than the AROME QPF. Nevertheless, fuzzy verification methods prove the contrary: for scales larger than 50 km, the AROME scores exceed the ALADIN scores, but not significantly. Copyright © 2008 Royal Meteorological Society

Inverse Problems in Wave Scattering

Since this was only a “half workshop”, the organisers intended to focus on a relatively small number of topics. We selected scattering from an obstacle and with primarily the Maxwell equations as the underlying structure. We also wanted to concentrate on analytic methods where questions of uniqueness and (if appropriate) existence were at the forefront. That is not to say that reconstructive methods were downplayed and in fact almost all of the talks did show some illustrative reconstructions, but greater attention was to the analysis of the methods rather than implementation issues. Even within this structure there was a strong focus on ideas that were in the spirit of sampling or factorization methods. These techniques work under a wide variety of physical situations and are perhaps optimal when very little is known a priori about the location, shape or material properties of the scatterer and where the most accurate solutions are not required. The participants were the usual geographical blend but what stood out in looking over the audience was the youthful median age. For many in the group this was their first visit to Oberwolfach and, in some cases, their first time to meet each other. With the smaller number of participants we were able to have a relaxed schedule and yet allow most to speak (about three quarters did so). Thus talks were scheduled for 9:30am, 11am, 4pm and 5pm except for the Wednesday and Friday when we only had morning talks. The duration was scheduled for a maximum of 40 minutes to allow for comments and discussions (and frequently the additional post-talk questions and comments took us up to the next speaker). We also had one evening session where a relatively new topic (the question of existence and properties of so called “transmission eigenvalues”) was discussed by the whole workshop. Almost all participants attended all the lectures; the exceptions were due to interest in talks from the other workshop. We thus conclude that the format was successful from the perspective of the participants. Another reason for the high attendance was the excellent overall quality of the talks; it is clear that most of the speakers had taken great care over the preparation and there was considerable evidence that talks were modified as the conference progressed in order to present a fresh set of ideas to the audience. It should be noted that at the last workshop run by the present organisers special sessions were held in the evening. One of these considered some new ideas on one of the oldest conjectures in the area – whether or not a single incident plane wave is sufficient to recover an arbitrary obstacle. There has been a history of partial results, but no substantial progress had been made on the problem for twenty years. Three of the participants at the previous meeting managed to make substantial progress in the year after the workshop. (The conjecture is still unproven in the most general setting but a first result proved the conjecture for convex, polygonal obstacles, a subsequent one removed the need for convexity. These used very different techniques and further progress is expected.) We think that devoting part of the time to look at a specific focused topic has enormous merit at Oberwolfach and we only hope for a similar outcome from the present workshop. As usual, the service provided by the staff was exemplary. This plays an enormous part in the “Oberwolfach experience” and allows the participants to concentrate on the research aspects. Finally, we should note that at the previous workshop the organisers complained about the state of current atmospheric modelling/computation leading to an erroneous weather forecast that added a surprise rainstorm to the Wednesday hike. We are sorry to report that the computational geophysicists still have work to do as they were again wrong, but we much prefer a forecast of rain yet in fact receive fine weather.

Short range forecast of convective rain using satellite data

The potential use of Meteosat Second Generation (MSG) clear air products in forecasts of convective rain over Germany in summer is examined. The three quantities Lifted Index (LI), Layer Precipitable Water (LPW ML ) of the Middle Layer and the trend of the Lifted Index (dLI/dt) are combined in a heuristic approach to gain a probabilistic forecast of convective rain with a lead time of three to six hours. Data from 33 days with a typical convective weather situation in summer 2005 and summer 2006 were used to derive the data pool necessary for the probabilistic forecast. The input parameter LI, LPW ML and dLI/dt were divided into 5, 4 and 5 classes, respectively. This leads to 100 possible combinations of the input parameters. For each set of input parameter, the information about rain three to six hours later is assigned. DWD radar data are used to detect the rain pattern three to six hours later. For typical summer convective days in Germany, a valuable probability forecast of convective rain is calculated for cloud-free areas.