scholarly journals Impact of the statistical method, training dataset, and spatial scale of post-processing to adjust ensemble forecasts of the height of new snow

2020 ◽  
Author(s):  
Nousu Jari-Pekka ◽  
Matthieu Lafaysse ◽  
Guillaume Evin ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Regional Scale ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Operational System ◽  
The Impact

<p>Forecasting the height of new snow (HS) is essential for avalanche hazard survey, road and ski resorts management, tourism attractiveness, etc. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, the SAFRAN downscaling tool refining the elevation resolution in mountains, and the Crocus snowpack model representing the main physical processes in the snowpack (compaction, melting, etc.). It provides better HS forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. Therefore, a post-processing is required to be able to provide automatic forecasting products of HS from this system.</p><p>For that purpose, we compare the skill of two statistical methods (Nonhomogeneous Regression with a Censored Shifted Gamma distribution and Quantile Regression Forest), two predictor datasets for training (22-year reforecast with some discrepancies with the operational system or 3-year real time forecasts similar to the operational system) and two spatial scales of post-processing (local scale or 1000 km² regional scale).</p><p>The improvement relative to the raw forecasts is similar at both spatial scales. Thus, the regional validity of post-processing does not restrict the application at points with observations. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to a discrepancy with the initial perturbations used in the operational system. Finally, thanks to a larger number of predictors, the Quantile Regression Forest allows an improvement of forecasts for specific cases when the the rain-snow transition elevation is overestimated by the raw forecasts.</p><p>These conclusions help to choose an optimal post-processing configuration for automatic forecasts of the height of new snow and encourage the atmospheric modelling teams to develop long reforecasts as homogenous as possible with the operational systems.</p>

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Vol 26 (3) ◽  
pp. 339-357 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

scholarly journals Large but decreasing effect of ozone on the European carbon sink

2018 ◽  
Vol 15 (13) ◽  
pp. 4245-4269 ◽  
Author(s):  
Rebecca J. Oliver ◽  
Lina M. Mercado ◽  
Stephen Sitch ◽  
David Simpson ◽  
Belinda E. Medlyn ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Primary Productivity ◽  
Carbon Sink ◽  
Spatial Scales ◽  
Stomatal Closure ◽  
Regional Scale ◽  
Surface Model ◽  
Background Concentrations ◽  
Tropospheric O3 ◽  
The Impact

Abstract. The capacity of the terrestrial biosphere to sequester carbon and mitigate climate change is governed by the ability of vegetation to remove emissions of CO2 through photosynthesis. Tropospheric O3, a globally abundant and potent greenhouse gas, is, however, known to damage plants, causing reductions in primary productivity. Despite emission control policies across Europe, background concentrations of tropospheric O3 have risen significantly over the last decades due to hemispheric-scale increases in O3 and its precursors. Therefore, plants are exposed to increasing background concentrations, at levels currently causing chronic damage. Studying the impact of O3 on European vegetation at the regional scale is important for gaining greater understanding of the impact of O3 on the land carbon sink at large spatial scales. In this work we take a regional approach and update the JULES land surface model using new measurements specifically for European vegetation. Given the importance of stomatal conductance in determining the flux of O3 into plants, we implement an alternative stomatal closure parameterisation and account for diurnal variations in O3 concentration in our simulations. We conduct our analysis specifically for the European region to quantify the impact of the interactive effects of tropospheric O3 and CO2 on gross primary productivity (GPP) and land carbon storage across Europe. A factorial set of model experiments showed that tropospheric O3 can suppress terrestrial carbon uptake across Europe over the period 1901 to 2050. By 2050, simulated GPP was reduced by 4 to 9 % due to plant O3 damage and land carbon storage was reduced by 3 to 7 %. The combined physiological effects of elevated future CO2 (acting to reduce stomatal opening) and reductions in O3 concentrations resulted in reduced O3 damage in the future. This alleviation of O3 damage by CO2-induced stomatal closure was around 1 to 2 % for both land carbon and GPP, depending on plant sensitivity to O3. Reduced land carbon storage resulted from diminished soil carbon stocks consistent with the reduction in GPP. Regional variations are identified with larger impacts shown for temperate Europe (GPP reduced by 10 to 20 %) compared to boreal regions (GPP reduced by 2 to 8 %). These results highlight that O3 damage needs to be considered when predicting GPP and land carbon, and that the effects of O3 on plant physiology need to be considered in regional land carbon cycle assessments.

scholarly journals A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

2007 ◽  
Vol 135 (4) ◽  
pp. 1424-1438 ◽  
Author(s):  
Andrew R. Lawrence ◽  
James A. Hansen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Ensemble Size ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Chaotic Model ◽  
The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

scholarly journals Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

2018 ◽  
Vol 33 (2) ◽  
pp. 599-607 ◽  
Author(s):  
John R. Lawson ◽  
John S. Kain ◽  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Dustan M. Wheatley ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Radar Data ◽  
Model Systems ◽  
Deterministic Systems ◽  
Forecast Lead Time ◽  
The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

scholarly journals On observational and modelling strategies targeted at regional carbon exchange over continents

2009 ◽  
Vol 6 (1) ◽  
pp. 1317-1343 ◽  
Author(s):  
C. Gerbig ◽  
A. J. Dolman ◽  
M. Heimann
Keyword(s):  
Spatial Scales ◽  
Regional Scale ◽  
Local Scale ◽  
Anthropogenic Sources ◽  
Carbon Exchange ◽  
Top Down ◽  
Bottom Up ◽  
Modelling Strategies ◽  
The Impact ◽  
Up Scaling

Abstract. Estimating carbon exchange at regional scales is paramount to understanding feedbacks between climate and the carbon cycle, but also to verifying climate change mitigation such as emission reductions and strategies compensating for emissions such as carbon sequestration. This paper discusses evidence for a number of important shortcomings of current generation modelling frameworks designed to provide regional scale budgets. Current top-down and bottom-up approaches targeted at deriving consistent regional scale carbon exchange estimates for biospheric and anthropogenic sources and sinks are hampered by a number of issues: We show that top-down constraints using point measurements made from tall towers, although sensitive to larger spatial scales, are however influenced by local areas much stronger than previously thought. On the other hand, classical bottom-up approaches using process information collected at the local scale, such as from eddy covariance data, need up-scaling and validation on larger scales. We therefore argue for a combination of both approaches, implicitly providing the important local scale information for the top-down constraint, and providing the atmospheric constraint for up-scaling of flux measurements. Combining these data streams necessitates quantifying their respective representation errors, which are discussed. The impact of these findings on future network design is highlighted, and some recommendations are given.

scholarly journals Regional-scale analysis of high-mountain multi-hazard and risk indicators in the Pamir (Tajikistan) with GRASS GIS

2013 ◽  
Vol 13 (11) ◽  
pp. 2779-2796 ◽  
Author(s):  
F. E. Gruber ◽  
M. Mergili
Keyword(s):  
Spatial Scales ◽  
Regional Scale ◽  
Risk Indicators ◽  
High Mountain ◽  
Scale Analysis ◽  
Model Framework ◽  
Rock Face ◽  
Grass Gis ◽  
Hazard And Risk ◽  
The Impact

Abstract. We present a model framework for the regional-scale analysis of high-mountain multi-hazard and -risk indicators, implemented with the open-source software package GRASS GIS. This framework is applied to a 98 300 km2 study area centred in the Pamir (Tajikistan). It includes (i) rock slides, (ii) ice avalanches, (iii) periglacial debris flows and (iv) lake outburst floods. First, a hazard indicator is assigned to each relevant object (steep rock face, glacier or periglacial slope, lake). This indicator depends on the susceptibility and on the possible event magnitude. Second, the possible travel distances, impact areas and, consequently, impact hazard indicators for all types of processes are computed using empirical relationships. The impact hazard indicators are finally superimposed with an exposure indicator derived from the type of land use, resulting in a raster map of risk indicators finally discretized at the community level. The analysis results are presented and discussed at different spatial scales. The major outcome of the study, a set of comprehensive regional-scale hazard and risk indication maps, shall represent an objective basis for the prioritization of target communities for further research and risk mitigation measures.

scholarly journals The impact of environmental factors on distribution of Scops Owl Otus scops in the wider area of Kras (SW Slovenia)

Acrocephalus ◽  
2011 ◽  
Vol 32 (148-149) ◽  
pp. 11-28 ◽  
Author(s):  
Tina Šušmelj
Keyword(s):  
Environmental Factors ◽  
Spatial Data ◽  
Goodness Of Fit ◽  
Agricultural Land ◽  
Population Distribution ◽  
Spatial Scales ◽  
Regional Scale ◽  
Goodness Of Fit Test ◽  
The Impact ◽  
Sw Slovenia

The impact of environmental factors on distribution of Scops Owl Otus scops in the wider area of Kras (SW Slovenia) The aim of the study was to determine the key environmental factors affecting Scops Owl Otus scops occurrence in the wider Kras plateau area (SW Slovenia, 665 km2). Scops Owl was systematically censused in 2006 (180 calling males) and in 2008 (167 calling males). Males were distributed either solitarily or clumped in groups, mostly situated in villages and its surroundings, indicating the species' synanthropic character. Crude densities were 0.3 males/km2 in 2006 and 2008, respectively, while ecological densities were 1.0 males/km2 in 2006 and 0.9 males/km2 in 2008. Population distribution remained roughly the same in both years, with the highest densities in the western and central parts of the Kras plateau, on Kraški rob and on Podgorski kras plateau. Habitat selection was analyzed at three spatial scales (regional, settlement and territory scales), based on spatial data layers (22 environmental variables), using Chi-square goodness-of-fit test and logistic regression. Results revealed that at the regional scale, Scops Owl preferably selected open habitats (extensively managed orchards, built-up areas, vineyards, permanent grasslands) and avoided dense forest and agricultural land with forest trees. As far as settlements were concerned, Scops Owl was more prone to select those that were more distant from the highway, with better preserved traditional agricultural landscape (with more hedgerows) and with higher average annual air temperature. In territory selection, Scops Owl occurrence was associated with longer distance from the highway, larger number of old buildings and higher landscape mosaics. The species seems to be threatened by traffic noise, habitat loss through abandonment and intensification of land and, potentially, by lack of breeding niches within settlements. Conservation measures should include the preservation of mosaic farmland, promotion of extensive agricultural practices, prevention of scrub and forest expansion, and maintenance of breeding niches (old trees, cavities in buildings).

scholarly journals Multi-scale hydrometeorological observation and modelling for flash-flood understanding

2014 ◽  
Vol 11 (2) ◽  
pp. 1871-1945 ◽  
Author(s):  
I. Braud ◽  
P.-A. Ayral ◽  
C. Bouvier ◽  
F. Branger ◽  
G. Delrieu ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Flash Flood ◽  
Spatial Scales ◽  
Regional Scale ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Ungauged Catchments ◽  
The Mediterranean ◽  
Set Up ◽  
The Impact

Abstract. This paper presents a coupled observation and modelling strategy aiming at improving the understanding of processes triggering flash floods. This strategy is illustrated for the Mediterranean area using two French catchments (Gard and Ardèche) larger than 2000 km2. The approach is based on the monitoring of nested spatial scales: (1) the hillslope scale, where processes influencing the runoff generation and its concentration can be tackled; (2) the small to medium catchment scale (1–100 km2) where the impact of the network structure and of the spatial variability of rainfall, landscape and initial soil moisture can be quantified; (3) the larger scale (100–1000 km2) where the river routing and flooding processes become important. These observations are part of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) Enhanced Observation Period (EOP) and lasts four years (2012–2015). In terms of hydrological modelling the objective is to set up models at the regional scale, while addressing small and generally ungauged catchments, which is the scale of interest for flooding risk assessment. Top-down and bottom-up approaches are combined and the models are used as "hypothesis testing" tools by coupling model development with data analyses, in order to incrementally evaluate the validity of model hypotheses. The paper first presents the rationale behind the experimental set up and the instrumentation itself. Second, we discuss the associated modelling strategy. Results illustrate the potential of the approach in advancing our understanding of flash flood processes at various scales.

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