Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

Kumar, Jitendra; Brooks, B.; Thornton, P. E.; Dietze, Michael C.

doi:10.1016/j.procs.2012.04.095

Cited by 32 publications

(25 citation statements)

References 21 publications

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“…Within meteorological driver data uncertainty, the role of uncertainty in the NOAA ensemble forecast is comparable to the role of uncertainty in downscaling the coarse-scale NOAA forecast to the local site using data from the meteorological station located at the reservoir. This highlights that future work should focus on evaluating whether more advanced downscaling methods, such as neutral networks [ Kumar et al 2012 ], can build better relationships between the NOAA forecasts and the local meteorological station.…”

Section: Discussionmentioning

confidence: 99%

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Thomas¹,

Figueiredo

Daneshmand

et al. 2020

Preprint

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We created a near-term iterative lake water temperature forecasting system that uses 13 sensors, data assimilation, and hydrodynamic modeling 14 15 • FLARE quantifies the uncertainty in each daily forecast and provides an open-source, 16 generalizable system for water quality forecasting 17 18• 16-day forecasted temperatures were within 0.91℃ over 100 days in a reservoir case Abstract 22 Freshwater ecosystems are experiencing greater variability due to human activities, necessitating 23 new tools to anticipate future water quality. In response, we developed and operationalized a 24 near-term iterative water temperature forecasting system (FLARE -Forecasting Lake And 25 Reservoir Ecosystems) that is generalizable for lakes and reservoirs. FLARE is composed of: 26 water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm 27 that uses sensor observations to update predictions from a hydrodynamic model and calibrate 28 model parameters, and an ensemble-based forecasting algorithm to generate forecasts that 29 include uncertainty. Importantly, FLARE quantifies the contribution of different sources of 30 uncertainty (parameters, driver data, initial conditions, and process) to each daily forecast of 31 water temperature at multiple depths. We applied FLARE to a temperate reservoir during a 100-32 day period that encompassed stratified and mixed thermal conditions and found that daily 33 forecasted water temperatures were on average within 0.91℃ at all depths of the reservoir over a 34 16-day forecast horizon. FLARE successfully predicted the onset of fall turnover eight days in 35 advance, and identified meteorology driver data and downscaling as the dominant sources of 36 forecast uncertainty. Overall, FLARE provides an open-source and easily-generalizable system 37 for water quality forecasting for lakes and reservoirs to improve management. 39Lake Model, Water temperature 41 45 freshwater ecosystems, which have been more degraded than any other ecosystem on the planet 46 [Millennium Ecosystem Assessment 2005], are seeking new tools to anticipate future change and 47 ensure clean water for drinking, fisheries, irrigation, industry, and recreation [Brookes et al. 48 2014]. 49 In response to this need, near-term iterative ecological forecasting has emerged as a 50 solution to provide stakeholders, managers, and policy-makers crucial information about future 51 ecosystem conditions [Clark et al. 2001, Dietze et al. 2018, Luo et al. 2011. Here, we define a 52 near-term iterative forecast as a projection of future ecosystem states with fully-specified 53 uncertainties, generated from predictive models that can be constantly updated with new data as 54 they become available [Clark et al. 2001]. Importantly, a near-term iterative forecast is not 55 created from merely one ecosystem simulation, but an ensemble of simulations that enable 56 quantification of the uncertainty in the forecast contributed by different sources [i.e., parameters, 57 driver data, initi...

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Section: Discussionmentioning

confidence: 99%

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Thomas¹,

Figueiredo

Daneshmand

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…From the results presented here, it can be reasoned that the downscaling or disaggregation of rainfall data, for instance from daily to hourly level, employing approaches that rely on statistical properties such as the variance of rainfall rates, may fail to capture metric properties of rainfall intermittency that are relevant to landsurface processes. For instance, statistical downscaling using neural net methods exhibited limited success with rainfall intensity, even when attempting to estimate depth per 6 h period (Kumar et al ., ). Likewise, in the context of climatic change, downscaling from regional climate models faces the challenge of how to correctly model the distribution of rain and dry periods, a problem that is of particular significance for urban runoff modelling (Olsson et al ., ).…”

Section: Discussionmentioning

confidence: 99%

Intra‐event intermittency of rainfall: an analysis of the metrics of rain and no‐rain periods

Dunkerley

2015

Hydrological Processes

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Abstract:The metric or 'observable' properties of intra-event rainfall intermittency (IERI) are quantified using a 10-year record from arid Fowlers Gap, Australia. Rainfall events were delineated using the minimum inter-event time (MIT) criterion, using eight values in the range of 1 h -24 h. Within events, no-rain periods were defined as corresponding to rainfall rates R < 0.1 mm/h or R < 0.2 mm/h (both less than typical wet-canopy evaporation rates during rainfall). In this way, rainfall events were subdivided into rain and no-rain periods. Intermittency was characterised using two measures: the fraction of rainless time within an event, and the duration of the longest rainless period. Events identified using a minimum inter-event time (MIT) of 24 h included on average 9.4 h of contiguous no-rain time (47.5% of the mean event duration), and only 6.8 h of contiguous rain. Total IERI averaged 51.1% for these events. Events defined with MIT = 6 h (a value commonly adopted in the literature) exhibited a mean of 1.53 h of no-rain and 9.04 h of contiguous rain. Total IERI averaged 13.9% for these events for R < 0.1 mm/h, but reached 39.2% if no-rain periods were taken as those of <0.2 mm/h. The maximum contiguous no-rain period for events defined using MIT = 6 h was 10.9 h from an event of 12.6 h duration, and this represents 86.5% of the event duration. Results demonstrate that smaller, shorter, and less intense rainfall events tend to exhibit higher IERI than larger, longer, and more intense events. IERI is relevant to the understanding of land surface processes. Information on the metric properties of IERI in different rainfall types (convective and stratiform) and rainfall climates (arid, maritime, and wet tropical) may prove to have significance for diverse studies in land surface hydrology.

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“…In particular, the development of the IDF relations for sub-daily extreme rainfalls is required for planning and design of urban drainage systems. In the existing literature there are only few approaches that have addressed the temporal downscaling scenario , Kumar et al 2012, Willems et al 2012) and most of the downscaling studies have been limited to spatial downscaling , Tripathi et al 2006 Therefore, the main objective of this study is to develop a combined approach of spatial and temporal downscaling to estimate sub daily extreme rainfalls for future periods. Further study aims to develop IDF curves to assist hydrologists in estimating design storms which are taking the future climate change into account.…”

Section: Introductionmentioning

confidence: 99%

A spatial temporal downscaling approach to development of IDF relations for Perth airport region in the context of climate change

Herath

Sarukkalige

Nguyen

2016

Hydrological Sciences Journal

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Downscaling of climate projections is the most adapted method to assess the impacts of climate change at regional and local scale. This study utilized both spatial and temporal downscaling approaches to develop Intensity Duration Frequency (IDF) relations for sub-daily rainfall extremes in Perth airport area. Multiple regression based SDSM tool was used to spatial downscaling of daily rainfalls using GCMs' (HadCM3 and CGCM3) climate variables. Simple scaling regime was identified for 30 minutes to 24 hours duration of observed Annual Maximum (AM) rainfalls. Therefore, statistical properties of sub daily AM rainfalls were estimated by scaling invariant model based on the GEV distribution. RMSE, Nash-Sutcliffe efficiency coefficient and Percentage bias values were estimated to check the accuracy of downscaled sub daily rainfalls. They proved the capability of proposed approach in developing linkage between large scale GCM's daily variables and extreme sub daily rainfall events at a given location. Finally IDF curves were developed for future periods and it show similar extreme rainfall decreasing trend for 2020s, 2050s and 2080s for both GCMs.

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Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

Cited by 32 publications

References 21 publications

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Intra‐event intermittency of rainfall: an analysis of the metrics of rain and no‐rain periods

A spatial temporal downscaling approach to development of IDF relations for Perth airport region in the context of climate change

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