QPF model for Sabarmati basin based on Synoptic analogue method

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Sabarmati basin based on 10 years data (2000-2009) during southwest monsoon period. The model was verified with the actual Average Areal Precipitation (AAP) for the corresponding synoptic situations during 2010.The performance of the model were observed Percentage Correct (PC) up to 71%. The cases out by one or two stage were due to variation in the intensity of the system especially upper air circulation (S3) over the basin. The synoptic analogue model was able to generate accurate QPF 24 hrs in advance to facilitate flood forecasters of Central Water Commission.

A synoptic analogue model for QPF of river Sabarmati basin

An attempt has been made to prepare a model for issuing semi quantitative precipitation forecast for river Sabarmati by synoptic analogue method. The model is based on 10 years (1986- 95) of data. The QPF issued by the model is verified with the WAR of years 1995 and 1996. The performance of model was good. This model can be used confidently for issue of QPF for Sabarmati basin.

QPF Model for lower Yamuna catchment, synoptic analogue method

The paper formulates a synoptic analogue model for issuing Quantitative Precipitation Forecast (QPF) for Lower Yamuna Catchment (LYC) based upon eleven years data (1998-2008) during southwest monsoon season. The results so derived were verified with realized Average Areal Precipitation (AAP) for the corresponding synoptic situation during 2009 southwest monsoon season. The performance of the model was observed Percentage Correct (PC) up to 86 % and for extreme events showed 100% correct with Heidke Skill Score (HSS) value 0.9. The experience during south west monsoon 2009 has shown that Synoptic analogue model can produce 24 hours advance QPF with accuracy and greater skill to facilitate the flood forecasters of Central Water Commission.

A Synoptic analogue model to issue QPF over Gangetic West Bengal and adjoining Jharkhand

In this study the Areal Average Precipitation (AAP) data for each day over each of the six catchments of Gangetic West Bengal (GWB) and adjoining Jharkhand namely river catchments of Mayurakhshi, Ajoy, Kansabati, Damodar, Barakar and Lower Valley of Damodar Valley Corporation during monsoon season for 25 years from 1990 to 2014 have been analyzed by grouping the AAP in three different ranges (11-25 mm, 26-50 mm, 51-100 mm and more), excluding Mainly Dry and 01-10 mm. The associated main synoptic features viz., trough at mean sea level, low pressure area, well marked low pressure area, cyclonic storm and cyclonic circulation for each day and their location with respect to the river catchments, viz., over the catchment, neighbourhood of the catchment (within 200 km South or North) and outside the catchment (more than 200 km South or North) have also been studied. The association of AAP ranges over six catchments with different categories of synoptic features has been examined. The distribution of percentage frequency of AAPs associated with the category of synoptic feature for the period 1990 to 2014 has led to development of a Synoptic Analogue Model (SAM) for issue of Quantitative Precipitation Forecast (QPF). The results obtained from SAM have been verified for rainfall data and calculated AAPs of monsoon season of 2015 over all the catchments and different skills scores also presented in this study.

Geometrical Breakdown Approach to interpretation of depositional sequences

Seismic and sequence stratigraphic analyses are important methodologies for interpreting coastal and shallow-marine deposits. Though both methods are based on objective criteria, terminology for reflection/stratal stacking is widely linked to eustatic cycles, which does not adequately incorporate factors such as differential subsidence, sediment supply, and autogenic effects. To reduce reliance on model-driven interpretations, we developed a Geometrical Breakdown Approach (GBA) that facilitates interpretation of horizon-bound reflection packages by systematically identifying upward-downward and landward-seaward trajectories of clinoform inflection points and stratal ter­minations, respectively. This approach enables a rigorous characterization of stratal surfaces and depositional units. The results are captured in three-letter acronyms that provide an efficient way of recognizing repetitive stacking pat­terns through discriminating reflection packages objectively to the maximum level of resolution provided by the data. Comparison of GBA with selected sequence stratigraphic models that include three and four systems tracts and the accommodation succession approach shows that the GBA allows a greater level of detail to be extracted, identifying key surfaces with more precision and utilizing more effectively the fine-scale resolution provided by the input seismic data. We tested this approach using a synthetic analogue model and field data from the New Jersey margin. The results demonstrate that the geometric criteria constitute a reliable tool for identifying systems tracts and provide an objective and straightforward method for practitioners at all levels of experience.

Heavy rainfall analysis over Teesta catchment and adjoining areas of Sub-Himalayan West Bengal and Sikkim

Heavy rainfall (HRF) forecasting in hilly region is always a challenge to the operational forecasters. Synoptic Analogue Model (SAM) is considered as one of the useful tools for HRF forecasting in topographically influenced hilly regions. In every monsoon season, the Teesta river catchment and its adjoining areas in Sub-Himalayan West Bengal and Sikkim (SHWB-S) generally receive several events of HRF. With the primary objective to find the method to issue HRF warning over Teesta river catchment and adjoining areas in SHWB-S, a SAM has been developed by analyzing 18 years (1998-2015) data comprising prevailing synoptic situations affecting the area and daily rainfall data of subsequent day of HRF. In addition, impact of different synoptic systems on the distribution of HRF has also been studied. The results revealed that there exists a good agreement between daily HRF warnings issued with the corresponding HRF event observed over this region on the next day.

A two-stage analogue model for real-time urban flood forecasting

<p>In this work a two-stage (rainfall nowcasting + flood prediction) analogue model for real-time urban flood forecasting is presented. The proposed approach accounts for the complexities of urban rainfall nowcasting while avoiding the expensive computational requirements of real-time urban flood forecasting.</p><p>The model has two consecutive stages:</p><ul><li><strong>(1) Rainfall nowcasting: </strong>0-6h lead time ensemble rainfall nowcasting is achieved by means of an analogue method, based on the assumption that similar climate condition will define similar patterns of temporal evolution of the rainfall. The framework uses the NORA analogue-based forecasting tool (Panziera et al., 2011), consisting of two layers. In the <strong>first layer, </strong>the 120 historical atmospheric (forcing) conditions most similar to the current atmospheric conditions are extracted, with the historical database consisting of ERA5 reanalysis data from the ECMWF and the current conditions derived from the US Global Forecasting System (GFS). In the <strong>second layer</strong>, twelve historical radar images most similar to the current one are extracted from amongst the historical radar images linked to the aforementioned 120 forcing analogues. Lastly, for each of the twelve analogues, the rainfall fields (at resolution of 1km/5min) observed after the present time are taken as one ensemble member. Note that principal component analysis (PCA) and uncorrelated multilinear PCA methods were tested for image feature extraction prior to applying the nearest neighbour technique for analogue selection.</li> <li><strong>(2) Flood prediction: </strong>we predict flood extent using the high-resolution rainfall forecast from Stage 1, along with a database of pre-run flood maps at 1x1 km<sup>2</sup> solution from 157 catalogued historical flood events. A deterministic flood prediction is obtained by using the averaged response from the twelve flood maps associated to the twelve ensemble rainfall nowcasts, where for each gridded area the median value is adopted (assuming flood maps are equiprobabilistic). A probabilistic flood prediction is obtained by generating a quantile-based flood map. Note that the flood maps were generated through rolling ball-based mapping of the flood volumes predicted at each node of the InfoWorks ICM sewer model of the pilot area.</li> </ul><p>The Minworth catchment in the UK (~400 km<sup>2</sup>) was used to demonstrate the proposed model. Cross‑assessment was undertaken for each of 157 flooding events by leaving one event out from training in each iteration and using it for evaluation. With a focus on the spatial replication of flood/non-flood patterns, the predicted flood maps were converted to binary (flood/non-flood) maps. Quantitative assessment was undertaken by means of a contingency table. An average accuracy rate (i.e. proportion of correct predictions, out of all test events) of 71.4% was achieved, with individual accuracy rates ranging from 57.1% to 78.6%). Further testing is needed to confirm initial findings and flood mapping refinement will be pursued.</p><p>The proposed model is fast, easy and relatively inexpensive to operate, making it suitable for direct use by local authorities who often lack the expertise on and/or capabilities for flood modelling and forecasting.</p><p><strong>References: </strong>Panziera et al. 2011. NORA–Nowcasting of Orographic Rainfall by means of Analogues. Quarterly Journal of the Royal Meteorological Society. 137, 2106-2123.</p>

Quantification of the relation between dynamical properties of meteorological variables and their predictability

<p>Local properties of chaotic systems can be summarized by dynamical indicators, that describe the recurrences of all states in phase space. Faranda et al. (2017) defined such indicators with the local dimension (d, approximating the local number of degrees of freedom of the system) and the inverse of persistence (θ, approximating the time it takes to leave a local state). It has been conjectured that such indicators give access to the local predictability of systems. The aim of this study is to evaluate how the predictability of climate variables such as temperature and precipitation is related to dynamical properties of the atmospheric flow.</p><p>The predictability of a chaotic system can be evaluated through ensembles of simulations, with probability scores (e.g. Continuous Rank Probability Score, CRPS). In this work, we consider ensembles of climate forecasts with a stochastic weather generator (SWG) based on analogs of atmospheric circulation (Yiou and Déandréis, 2019). We are interested in relating predictability scores of European temperatures and precipitation, obtained with this SWG, and the local dynamical properties of the synoptic atmospheric circulation, obtained from the NCEP reanalysis. We show experimentally that the CRPS of local climate variables can be predicted from large-scale (d, \ θ) values of geopotential height fields, for time leads of 5 to 10 days. A practical application is that the predictability of local variables (in Europe) can be anticipated from large-scale dynamical quantities, which can help to dimension the size of ensemble forecasts.</p><p><strong>References</strong></p><p>Faranda, D., Messori, G., Yiou, P., 2017. Dynamical proxies of North Atlantic predictability and extremes. Sci. Rep. 7, 41278. https://doi.org/10.1038/srep41278</p><p>Caby, T. Extreme Value Theory for dynamical systems, with applications in climate and neuroscience. Mathematics [math]. Université de Toulon Sud; Universita dell’Insubria, 2019. English.tel-02473235v1</p><p>Yiou, P., Déandréis, C., 2019. Stochastic ensemble climate forecast with an analogue model. Geosci. Model Dev. 12, 723–734. https://doi.org/10.5194/gmd-12-723-2019</p><p><strong> </strong></p><p><strong>Acknowledgments</strong></p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p><p> </p>