ORCID Profile
0000-0002-0613-5462
Current Organisation
Finnish Environment Institute
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Publisher: Wiley
Date: 16-02-2017
DOI: 10.1002/HYP.11126
Publisher: Elsevier BV
Date: 11-2021
Publisher: Informa UK Limited
Date: 24-05-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2019
Publisher: Springer International Publishing
Date: 09-2018
Publisher: Copernicus GmbH
Date: 19-06-2020
DOI: 10.5194/HESS-24-3157-2020
Abstract: Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different s ling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in s ling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.
Publisher: American Meteorological Society
Date: 12-2021
DOI: 10.1175/JTECH-D-21-0013.1
Abstract: Delivering reliable nowcasts (short-range forecasts) of severe rainfall and the resulting flash floods is important in densely populated urban areas. The conventional method is advection-based extrapolation of radar echoes. However, during rapidly evolving convective rainfall this so-called Lagrangian persistence (LP) approach is limited to deterministic and very short-range nowcasts. To address these limitations in the 1-h time range, a novel extension of LP, called Lagrangian Integro-Difference equation model with Autoregression (LINDA), is proposed. The model consists of five components: 1) identification of rain cells, 2) advection, 3) autoregressive process describing growth and decay of the cells, 4) convolution describing loss of predictability at small scales, and 5) stochastic perturbations to simulate forecast uncertainty. Advection is separated from the other components that are applied in the Lagrangian coordinates. The reliability of LINDA is evaluated using the NEXRAD WSR-88D radar that covers the Dallas–Fort Worth metropolitan area, as well as the NEXRAD mosaic covering the continental United States. This is done with two different configurations: LINDA-D for deterministic and LINDA-P for probabilistic nowcasts. The validation dataset consists of 11 rainfall events during 2018–20. For predicting moderate to heavy rainfall (5–20 mm h −1 ), LINDA outperforms the previously proposed LP-based approaches. The most significant improvement is seen for the ETS and POD statistics with the 5 mm h −1 threshold. For 30-min nowcasts, they show 15% and 16% increases, respectively, to the second-best method and 48% and 34% increases compared to LP. For the 5 mm h −1 threshold, the increase in the relative operating characteristic (ROC) skill score of 30-min nowcasts from the second-best method is 10%. Delivering reliable forecasts of severe rainfall for the next few hours has a major societal importance. This is particularly true for densely populated urban areas, where flash floods can cause property damage and loss of lives. Such forecasts are conventionally produced by direct extrapolation of weather radar measurements. However, for intense localized rainfall this approach has low prediction ability beyond 30 min. To extend this limit, we propose a novel method that combines machine vision with a statistical model for growth and decay of rainfall. The method is designed for predicting highly localized rain cells and bands. In addition, a stochastic extension for producing probabilistic forecasts is developed. Using several verification metrics, we demonstrate that for predicting moderate to heavy rainfall (5–20 mm h −1 ), the proposed method has significantly improved forecast skill compared to the reference methods. The evaluation is done by using the NEXRAD WSR-88D that covers the Dallas–Fort Worth urban metroplex with a population of over 7 million. Demonstration of the applicability of LINDA in a larger domain is done by using the NEXRAD radar network that covers the continental United States.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-8194
Abstract: & & The presentation highlights the TAMIR project (2020-2022), its mid-term results, and final objectives.& & & & Hazards created by convective storms and heavy rainfall, e.g. flooding, turn into disasters when and where they encounter exposed and vulnerable societal systems, e.g. human life and activities, assets, and infrastructure. The recent progress in probabilistic multi-source rainfall-induced hazard forecasting has enabled predictions from the nowcast (minutes) to short-medium ranges (5 days), allowing for consistent decision making at both emergency response and planning stages. Nevertheless, civil protection agencies still face challenges that h er their ability to make active decisions when preparing for emergencies in severe weather situations. The challenges include e.g. high false-alarm rates, lack of multi-hazard forecasts (e.g. the combined effects of heavy rainfall, flood, lightings, wind gusts, hail), difficulties in translating the hazard forecasts into impact forecasts, and inadequate risk assessments. The TAMIR project, funded by the EU Civil Protection Mechanism, addresses these challenges using innovative, state-of-the-art science, and integration of the developed tools and services into existing systems. Experimental additional products are delivered e.g. via the European Flood Awareness System (EFAS) platform, part of the Copernicus Emergency Management Service, and as new information in regional civil protection systems.& & & & In the project, pro-active emergency management is supported by developing forecast products covering different spatial scales (regional to pan-European) and lead times (15 minutes to 5 days). In particular, the project focuses on improving existing products and tools with enhanced impact assessment and preparedness capacity. The uncertainty related to precipitation type in flood forecasts is considered by utilizing a model-based precipitation type estimate to guide the radar-based flood hazard estimate, as snowfall is far less prone to cause severe flood hazard than rainfall. Flash flood hazard forecasting is improved by developing lead-time dependent flood warning thresholds, utilizing the information on precipitation type and the information from gauge adjusted EUMETENET OPERA weather radar composite data and NWP data. Flood risk assessments are improved by combining the flash flood hazard forecasts with enhanced vulnerability and exposure data, covering information about population, transportation infrastructure, energy infrastructure, education facilities and health facilities, and by developing methods to turn the combined information into improved flood rapid risk impact assessments. To account for hazards and risks caused by convective weather, a nowcasting tool for multi-hazards caused by thunderstorms is being developed which combines a cell-based storm nowcast model with a classification model that estimates the hazard level of convective storm situations based on historical data on meteorological observations and the emergency calls they have caused. The multi-hazard nowcasts are again combined with vulnerability layers to produce risk nowcasts for damages from thunderstorms.& & & & Another important aspect of supporting pro-active emergency management is integrating the products and tools developed in the project to operational platforms. Accordingly, the developed products are delivered to end-users utilizing e.g. the EFAS platform and integration into existing civil protection platforms as new web services. This allows for assessing the usefulness of the products and further refinements based on end-user experiences.& &
Publisher: Informa UK Limited
Date: 25-12-2019
Publisher: Informa UK Limited
Date: 07-02-2019
Publisher: American Geophysical Union (AGU)
Date: 07-2014
DOI: 10.1002/2013WR015190
Publisher: Elsevier BV
Date: 04-2017
Publisher: American Geophysical Union (AGU)
Date: 2016
DOI: 10.1002/2015WR017521
Publisher: Springer International Publishing
Date: 09-2018
Publisher: Elsevier BV
Date: 04-2022
DOI: 10.1016/J.SCITOTENV.2021.152855
Abstract: Urban hydrology is characterized by increased runoff and various pollutant sources. We studied the spatio-temporal patterns of stormwater metal (Al, V, Cr, Mn, Fe, Cu, Zn, and Pb) concentrations and loads in five urbanized and one rural catchment in Southern Finland. The two-year continuous monitoring revealed a non-linear seasonal relationship between catchment urban intensity and metal export. For runoff, seasonal variation decreased with increasing imperviousness. The most urbanized catchments experienced greatest temporal variation in metal concentrations: the annual Cu and Zn loads in most of the studied urbanized catchments were up to 86 times higher compared to the rural site, whereas Fe loads in the urbanized catchments were only circa 29% of the rural load. Total metal levels were highest in the winter, whereas the winter peak of dissolved metal concentrations was less pronounced. The collection of catchment characteristics explained well the total metal concentrations, whereas for the dissolved concentrations the explanatory power was weaker. Our catchment-scale analysis revealed a mosaic of mainly diffuse pollutant sources and calls for catchment-scale management designs. As urban metal export occurred across seasons, solutions that operate also in cold conditions are needed.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-6618
Abstract: & & Convective storms and long-lasting mesoscale convective systems have the potential to cause heavy rainfall, flooding, hail, wind gusts and lightning that can result in significant damage to property and loss of lives. Accurate prediction of the location or severity of such storms (e.g. in the sub-kilometer resolution for the next hour) to assist the decision-making of civil protection authorities is beyond the capabilities of the current numerical weather prediction models. Thus, weather radar and machine learning-based methods provide an important tool to predict such events and their impacts in advance. Identifying a storm cell or system as an & #8220 object& #8221 from a radar image provides a natural way for associating different meteorological attributes of a storm with its impacts. In the TAMIR project funded by the EU Civil Protection Mechanism, we have implemented this by combining a cell tracking system with a machine learning model. The hazard levels of storms are estimated from their distance and time delay to the associated emergency reports obtained from the PRONTO database provided by the Finnish civil protection authorities. Using several meteorological attributes related to severe weather (e.g. lightning flash, hail and wind observations and indicators of convective potential), a random forest model was trained for predicting the storm hazard level. This was done by using a large s le of data during summer months between 2013-2020. The model for predicting the hazard level was verified by cross-validation. A Kalman filter-based methodology was applied for probabilistic nowcasting of future storm locations, which was combined with the model for hazard level prediction. Finally, the hazard nowcasts were combined with different exposure layers to translate them into prediction of impacts caused by convective storms. In the presentation, we demonstrate the added value of the implemented hazard and impact nowcast products with case studies. The products have also been evaluated by the Finnish civil protection authorities during the test period June-September 2021 with largely positive feedback. While the feasibility of the proposed methodology is demonstrated in Finland, discussion about its transferability to other parts of the world is also given.& &
Publisher: Copernicus GmbH
Date: 15-08-2019
Abstract: Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by moving to higher resolution and making use of dual-polarization, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. But since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 hours. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, 6 different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events for each country were used to quantify the overall agreement between radar and gauges and the errors affecting the peaks. Results show that the overall agreement between radar and gauges in heavy rain is fair, with multiplicative biases in the order of 1.41–1.66 (i.e., radar underestimates by 29–39.8 %) and correlation coefficients of 0.71–0.83 across countries. However, the bias increases with intensity, reaching 45.9 %–66.2 % during the peaks. Only part of the bias (i.e., roughly 13 %–30 % depending on the radar product) can be explained by differences in measurement areas between gauges and radar. Radar products with higher spatial and temporal resolutions agreed better with the gauges, highlighting the importance of high-resolution radar for urban hydrology. However, for capturing peak intensity and reducing the bias during the most intense part of a storm, the ability to combine measurements from multiple overlapping radars to help mitigate attenuation seemed to play a more important role than resolution. The use of dual-polarization and phase information (e.g., Kdp) in the experimental Finnish OSAPOL product also seemed to provide a slight advantage in heavy rain. But improvements were hard to quantify and similarly good results were achieved in the Netherlands by applying a simple Z–R relation together with a mean field bias-correction.
Publisher: MDPI AG
Date: 27-08-2023
DOI: 10.3390/W15173066
Abstract: Current design storms used in hydrological modeling, urban planning, and dimensioning of structures are typically point-scale rainfall events with a steady rainfall intensity or a simple temporal intensity pattern. This can lead to oversimplified results because real rainfall events have more complex patterns than simple design series. In addition, the interest of hydrologists is usually in areal estimates rather than point values, most commonly in river-basin-wide areal mean rainfall estimates. By utilizing weather radar data and the short-term ensemble prediction system pySTEPS, which has so far been used for precipitation nowcasting, ensembles of high-resolution stochastic design storms with desired statistical properties and spatial structure evolving in time are generated. pySTEPS is complemented by adding time-series models for areal average rainfall over the simulation domain and field advection vectors. The selected study area is the Kokemäenjoki river basin located in Western Finland, and the model parametrization is carried out utilizing the Finnish Meteorological Institute’s weather radar data from the years 2013 to 2016. The results demonstrate how simulated events with similar large-scale mean areal rainfall can produce drastically different total event rainfalls in smaller scales. The s ling method, areal vs. gauge estimate, is also shown to have a prominent effect on total event rainfall across different spatial scales. The outlined method paves the way towards a more thorough and wide-spread assessment of the hydrological impacts of spatiotemporal rainfall characteristics.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-12707
Abstract: & & As part of TAMIR, a European Commission Civil Protection Preparedness project (ID. 874435), probabilistic operational pan-European flash flood impact forecasts with lead times from 0 to 120 hours have been developed by combining flash flood hazard forecasts with exposure data. Working with civil protection agencies, the aim is to develop forecasts which clearly identify the areas most at risk of serious impacts and therefore may require their intervention. Firstly, the project engaged with these agencies to identify their requirements of flash flood impact forecasts and which elements of exposure and important to them when assessing impacts. Accordingly, pan-European exposure data for population and critical infrastructure (health, education, transport, and energy generation facilities) were sourced from several open source datasets (HARCI-EU, OSM, GHS). These exposure data were (if necessary) regridded and cropped to the spatial domain, transformed to reduce skewness, and rescaled between 1 and 2 to give the datasets common units. The five exposure types were then added together and re-scaled, to produce a combined exposure layer with values ranging from 1 (low exposure) to 2 (high exposure). Flash flood hazard forecasts were created in a previous project by blending hourly ensemble precipitation nowcasts with ensemble numerical weather predictions (NWP) from the ECMWF IFS (Integrated Forecast System). These forecasts are created once per hour and have a lead time of up to 5 days. The flash flood impact forecasts were created by combining the hazard forecasts and exposure data on a two-dimensional impact matrix. Both axes of matrix are split into 3 categories (low, medium, high). For exposure, the ranges for each category were chosen based on the distribution of the data. For hazard, the low, medium, and high categories indicate where the forecast probability shows a 5%-50%, 50%-80%, and 80%+ likelihood of exceeding the 5-year return period threshold.& & & & Once developed, the impact forecasts were applied to 6 case studies of single flash flooding events across Europe chosen by the civil protection agencies, and the results presented to them. This helped evaluate the impact forecasts and enabled end users to provide feedback for further improvement. Results indicated the impact forecasts provided considerable added value compared to the hazard forecasts, by identifying targeted areas where serious impacts were observed. In the final stages of the project, the methods and products described here will be implemented in the European Flood Awareness System (EFAS) platform as a quasi-operational experimental product, and made available to the wider scientific community in the form of a Web Map Service Time (WMS-T) layer. Overall, this presentation focuses on the creation and communication of the exposure data and subsequent impact forecasts. Additionally, it outlines the evaluation of the impact forecasts, and the benefits obtained from engaging end users throughout the process. Finally, it highlights some of the challenges of using pan-European data and a continental scale forecast system to provide impact forecasts useful at the smaller scales required by decision makers.& &
No related grants have been discovered for Tero Niemi.