Model Predictive Control For Real Time Operation Of Hydraulic Structures For Draining The Operational Area Of The Dutch Water Authority Noorderzijlvest

A pilot project in 2012 for the Dutch regional water authority Noorderzijlvest has shown that the application of Model Predictive Control (MPC) can increase the safety level of the water system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this paper, the extension of the pilot to a real time decision support system is presented. It supports the daily operation of 34 aggregated structures both in wet and dry periods by providing optimal control settings through the application of MPC. We explain the improved prediction model that is accurate and fast enough for optimization purposes, and how it is integrated in the operational flood early warning system. Besides the prediction model, the weights of the individual objective function terms are an important element of MPC, since they shape the overall control objective. We developed special features in the forecasting system to permit the operators to adjust the objective function with respect to seasonal changes in order to evaluate different control strategies

Operational low-flow forecasting using LSTMs

This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and forecasted meteorological data to carry out multi-step discharge time series forecasting. The feature and target selection for this deep learning (DL) model involves evaluating the use of different spatial resolutions for meteorological forcing (basin-averaged or subbasin-averaged), the impact of incorporating past discharge observations, and the use of different target variables (discharge Q or time-differenced discharge dQ). Then, the model is trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast (SEAS5) data. The forecast results are compared to a benchmark process-based model, wflow_sbm. This study also explores the flexibility of the DL model by fine-tuning the pretrained model with limited SEAS5 dataset. Key findings from feature and target selection include: (1) opting for subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach. (2) Utilizing dQ as the target variable greatly boosts short-term forecast accuracy compared to using Q, with a mean absolute error (MAE) of 25 m3 s−1 and mean absolute percentage error (MAPE) of 0.02 for the first lead time, ensuring reliability and accuracy at the onset of the forecast horizon. (3) While incorporating historical discharge improves the forecasting of Q, its impact on predicting dQ is less pronounced for short lead times. In the operational forecast with SEAS5, compared to the wflow_sbm model, the DL model exhibits skill in forecasting low flows as evidenced by Continuous Ranked Probability Skill Score (CRPSS) median values of all lead times above zero, and better accuracy in forecasting drought events within short lead times. The wflow_sbm model shows higher accuracy for longer lead times. In the exploration of fine-tuning approach, the fine-tuned model generates marginal short-term enhancements in forecasting low-flow events over a non-fine-tuned model. Overall, this study contributes to advancing the field of low-flow forecasting using deep learning approach

WDR5, BRCA1, and BARD1 Co-regulate the DNA Damage Response and Modulate the Mesenchymal-to-Epithelial Transition during Early Reprogramming.

Differentiated cells are epigenetically stable, but can be reprogrammed to pluripotency by expression of the OSKM transcription factors. Despite significant effort, relatively little is known about the cellular requirements for reprogramming and how they affect the properties of induced pluripotent stem cells. We have performed high-content screening with small interfering RNAs targeting 300 chromatin-associated factors and extracted colony-level quantitative features. This revealed five morphological phenotypes in early reprogramming, including one displaying large round colonies exhibiting an early block of reprogramming. Using RNA sequencing, we identified transcriptional changes associated with these phenotypes. Furthermore, double knockdown epistasis experiments revealed that BRCA1, BARD1, and WDR5 functionally interact and are required for the DNA damage response. In addition, the mesenchymal-to-epithelial transition is affected in Brca1, Bard1, and Wdr5 knockdowns. Our data provide a resource of chromatin-associated factors in early reprogramming and underline colony morphology as an important high-dimensional readout for reprogramming quality.V.B. and C.B. are funded by the Stand Up to Cancer campaign for Cancer Research UK, and Cancer Research UK Program Foundation Award to C.B. (C37275/1A20146). K.M. was supported by an NWO-VIDI grant (864.12.010)

CARROTS: een klimatologische correctie voor radarneerslag in een operationele context : CARROTS: A climatological correction product for radar rainfall in an operational setting

Real-time radar quantitative precipitation estimations (QPEs) generally show significant biases from the true rainfall amount. Despite the abundant number of adjustment methods, the absence of a timely reporting high-density rain gauge network limits the use of these methods. This especially holds for more advanced geostatistical and Bayesian methods that can also correct the radar QPE in space. As an alternative, we present CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a gridded climatological QPE correction product, which corrects the radar QPE both in space and time. The publicly available CARROTS factors are based on a historical set of 10 years of 5-min radar and reference rainfall data from KNMI, which makes CARROTS independent of real-time rain gauge availability. We tested the CARROTS factors on the resulting corrected radar QPE and subsequent discharge simulations for 12 Dutch catchments. We regarded the mean field bias (MFB) adjustment method as benchmark in this study. This real-time adjustment method determines spatially uniform adjustment factors based on the 32 automatic rain gauges of KNMI and is operationally used in the Netherlands. The CARROTS factors show clear spatial and temporal patterns. From December through March, the factors are higher than in other seasons, which is likely a result of sampling above the melting layer during these months. Compared to the unadjusted radar QPE, both adjustment methods significantly improve the estimated rainfall sums, but annual rainfall sums from CARROTS outperform the MFB-adjusted QPE for catchments in the south and east of the Netherlands. In these regions, the MFB-adjusted QPE still underestimates the rainfall amounts. Differences in the rainfall estimations are amplified in the discharge simulations, where CARROTS outperforms the simulations with the MFB-adjusted product for all but one basin. Concluding, CARROTS can be a benchmark for QPE adjustment method development, and it has shown to be a better operational option than the MFB method, provided that the radar data it is used on, was processed in a similar way as the radar data the factors are based on

Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting: Adjustment factors for the Netherlands

This dataset contains gridded adjustment factors for correction of the Quantitative Precipitation Estimations (QPE) of the two operational C-band weather radars operated by the Royal Netherlands Meteorological Institute (KNMI). The factors are based on the CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting) method, described in Imhoff et al. (2021). The factors are available for every yearday (temporal resolution of one day) and are based on ten years (2009 - 2018) of radar and reference rainfall data, as distributed by KNMI. For the derivation of the factors, both the operational radar QPE (https://doi.org/10.4121/uuid:05a7abc4-8f74-43f4-b8b1-7ed7f5629a01) and a reference rainfall dataset of KNMI (https://dataplatform.knmi.nl/catalog/datasets/index.html?x-dataset=rad_nl25_rac_mfbs_em_5min&&x-dataset-version=2.0) are used. The reference is not available in real time, but becomes available with a one to two month delay and was therefore available for this climatological factor derivation. The derivation method was as follows per grid cell in the radar domain (Imhoff et al., 2021): 1. For every day in the period 2009--2018, an accumulation took place of all 5-min rainfall sums (of both the unadjusted radar QPE and the reference) within a moving window of 15 days prior to and 15 days after the day of interest. 2. For every yearday, the accumulations (per day) from the previous step were averaged over the ten years. 3. Gridded climatological adjustment factors (Fclim) were calculated per yearday as: Fclim(i,j) = RA(i,j) / RU(i,j). In this equation, RA(i,j) is the reference rainfall sum for the ten years and RU(i,j) the operationally available unadjusted radar QPE sum, based on the previous two steps, at grid cell (i, j). For more details about the method, see Imhoff et al. (2021). For more information about the reference dataset, which consists of the radar QPE spatially adjusted with observations from 31 automatic and 325 manual rain gauges, see Overeem et al. (2009a,b)

A climatological benchmark for operational radar rainfall bias reduction

The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5ĝ€¯min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week. Water Resource

Scale-dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open-source pysteps library

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation-based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short-term rainfall forecasts, which should increase decision-making times. We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library, based on the Short-Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill-dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48-member blended forecasts against the Belgian NWP forecast, a 48-member nowcast, and a simple 48-member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems.</p

WDR5, BRCA1, and BARD1 Co-regulate the DNA Damage Response and Modulate the Mesenchymal-to-Epithelial Transition during Early Reprogramming

Contains fulltext : 202765.pdf (publisher's version ) (Open Access

Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting: Adjustment factors for the Netherlands

This dataset contains gridded adjustment factors for correction of the Quantitative Precipitation Estimations (QPE) of the two operational C-band weather radars operated by the Royal Netherlands Meteorological Institute (KNMI). The factors are based on the CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting) method, described in Imhoff et al. (2021). The factors are available for every yearday (temporal resolution of one day) and are based on ten years (2009 - 2018) of radar and reference rainfall data, as distributed by KNMI. For the derivation of the factors, both the operational radar QPE (https://doi.org/10.4121/uuid:05a7abc4-8f74-43f4-b8b1-7ed7f5629a01) and a reference rainfall dataset of KNMI (https://dataplatform.knmi.nl/catalog/datasets/index.html?x-dataset=rad_nl25_rac_mfbs_em_5min&&x-dataset-version=2.0) are used. The reference is not available in real time, but becomes available with a one to two month delay and was therefore available for this climatological factor derivation.The derivation method was as follows per grid cell in the radar domain (Imhoff et al., 2021):1. For every day in the period 2009--2018, an accumulation took place of all 5-min rainfall sums (of both the unadjusted radar QPE and the reference) within a moving window of 15 days prior to and 15 days after the day of interest. 2. For every yearday, the accumulations (per day) from the previous step were averaged over the ten years.3. Gridded climatological adjustment factors (Fclim) were calculated per yearday as: Fclim(i,j) = RA(i,j) / RU(i,j). In this equation, RA(i,j) is the reference rainfall sum for the ten years and RU(i,j) the operationally available unadjusted radar QPE sum, based on the previous two steps, at grid cell (i, j).For more details about the method, see Imhoff et al. (2021). For more information about the reference dataset, which consists of the radar QPE spatially adjusted with observations from 31 automatic and 325 manual rain gauges, see Overeem et al. (2009a,b). <br