7 research outputs found
Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)
Quantitative precipitation nowcasting (QPN) has become an
essential technique in various application contexts, such as early warning or
urban sewage control. A common heuristic prediction approach is to track the
motion of precipitation features from a sequence of weather radar images and
then to displace the precipitation field to the imminent future (minutes to
hours) based on that motion, assuming that the intensity of the features
remains constant (“Lagrangian persistence”). In that context, “optical
flow” has become one of the most popular tracking techniques. Yet the
present landscape of computational QPN models still struggles with producing
open software implementations. Focusing on this gap, we have developed and
extensively benchmarked a stack of models based on different optical flow
algorithms for the tracking step and a set of parsimonious extrapolation
procedures based on image warping and advection. We demonstrate that these
models provide skillful predictions comparable with or even superior to
state-of-the-art operational software. Our software library (“rainymotion”)
for precipitation nowcasting is written in the Python programming language
and openly available at GitHub (https://github.com/hydrogo/rainymotion,
Ayzel et al., 2019). That way,
the library may serve as a tool for providing fast, free, and transparent
solutions that could serve as a benchmark for further model development and
hypothesis testing – a benchmark that is far more advanced than the
conventional benchmark of Eulerian persistence commonly used in QPN
verification experiments.</p
Применение методов машинного обучения для моделирования толщины снежного покрова
Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France), Sodankyla (Finland), and Snoquamie Pass (USA).Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.На основе открытых данных гидрометеорологических наблюдений на трёх водно-балансовых стационарах, расположенных в различных физико-географических условиях, исследована возможность применения современных методов машинного обучения для моделирования динамики снежного покрова. Эффективность использования ансамблевой модели градиентного бустинга над решающими деревьями выше, чем моделей одиночного решающего дерева или адаптивного бустинга для всех исследуемых объектов
Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea
The Aral Sea desiccation and related changes in hydroclimatic conditions on a
regional level is a hot topic for past decades. The key problem of scientific
research projects devoted to an investigation of modern Aral Sea basin
hydrological regime is its discontinuous nature – the only limited amount of
papers takes into account the complex runoff formation system entirely.
Addressing this challenge we have developed a continuous prediction system
for assessing freshwater inflow into the Small Aral Sea based on coupling
stack of hydrological and data-driven models. Results show a good prediction
skill and approve the possibility to develop a valuable water assessment tool
which utilizes the power of classical physically based and modern machine
learning models both for territories with complex water management system and
strong water-related data scarcity. The source code and data of the proposed
system is available on a Github page
(https://github.com/SMASHIproject/IWRM2018)
Use of machine learning techniques for modeling of snow depth
Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France), Sodankyla (Finland), and Snoquamie Pass (USA).Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient
Climate change impact on streamflow in large-scale river basins: projections and their uncertainties sourced from GCMs and RCP scenarios
Climate change impact on river runoff was investigated
within the framework of the second phase of the Inter-Sectoral Impact Model
Intercomparison Project (ISI-MIP2) using a physically-based land surface
model Soil Water – Atmosphere – Plants (SWAP) (developed in the Institute
of Water Problems of the Russian Academy of Sciences) and meteorological
projections (for 2006–2099) simulated by five General Circulation Models
(GCMs) (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and
NorESM1-M) for each of four Representative Concentration Pathway
(RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Eleven large-scale
river basins were used in this study. First of all, SWAP was calibrated and
validated against monthly values of measured river runoff with making use of
forcing data from the WATCH data set and all GCMs' projections were
bias-corrected to the WATCH. Then, for each basin, 20 projections of
possible changes in river runoff during the 21st century were simulated
by SWAP. Analysis of the obtained hydrological projections allowed us to
estimate their uncertainties resulted from application of different GCMs and
RCP scenarios. On the average, the contribution of different GCMs to the
uncertainty of the projected river runoff is nearly twice larger than the
contribution of RCP scenarios. At the same time the contribution of GCMs
slightly decreases with time
Impact of possible climate changes on river runoff under different natural conditions
The present study was carried out within the framework of the International
Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) for 11 large
river basins located in different continents of the globe under a wide
variety of natural conditions. The aim of the study was to investigate
possible changes in various characteristics of annual river runoff (mean
values, standard deviations, frequency of extreme annual runoff) up to 2100
on the basis of application of the land surface model SWAP and meteorological
projections simulated by five General Circulation Models (GCMs) according to
four RCP scenarios. Analysis of the obtained results has shown that changes
in climatic runoff are different (both in magnitude and sign) for the river
basins located in different regions of the planet due to differences in
natural (primarily climatic) conditions. The climatic elasticities of river
runoff to changes in air temperature and precipitation were estimated that
makes it possible, as the first approximation, to project changes in climatic
values of annual runoff, using the projected changes in mean annual air
temperature and annual precipitation for the river basins. It was found that
for most rivers under study, the frequency of occurrence of extreme runoff
values increases. This is true both for extremely high runoff (when the
projected climatic runoff increases) and for extremely low values (when the
projected climatic runoff decreases)