Simulation of Spatial Temperature-Precipitation Compound Events with Circulation-Conditioned Weather Generator

2020 ◽  
Author(s):  
Martin Dubrovsky ◽  
Ondrej Lhotka ◽  
Jiri Miksovsky
Keyword(s):  
Weather Generator ◽  
European Regions ◽  
Time Step ◽  
Temporal Scales ◽  
Stochastic Weather Generator ◽  
Fire Weather Index ◽  
Spatial And Temporal Scales ◽  
Temperature And Precipitation ◽  
Surface Weather ◽  
Spatial Temperature

<p>GRIMASA project aims to develop a spatial (not only, but especially a gridded version) stochastic weather generator (WG) applicable at various spatial and temporal scales, for both present and future climates. The multi-purpose SPAGETTA generator (Dubrovsky et al, 2019, Theoretical and Applied Climatology) being developed within this project is based on a parametric approach suggested by Wilks (1998, 2009). It was presented already at EGU-2017 and EGU-2018 conferences. It is run mainly at daily time step and allows to produce multivariate weather series for up to 100 (approximately) grid-points. In developing and validating the generator, we employ also various compound weather indices defined by multiple weather variables, which allows to account for the inter-variable correlations in the validation process. In our first experiments, the WG was run at 100 km resolution (50 km EOBS data were used for calibrating the WG) for eight European regions, and its performance was compared with RCMs (CORDEX simulations for EUR-44 domain). In our EGU-2019 contribution, our WG was validated in terms of characteristics of spatial temperature-precipitation compound spells (including dry-hot spells). Most recently, after implementing wind speed and humidity into the generator, the WG was run at much finer resolution (using data from irregularly distributed weather stations in Czechia and Sardinia) and validated in terms of spatial spells of wildfire-prone weather (using Fire Weather Index) (results were presented at AGU-2019).</p><p> </p><p>Present project activities aim mainly at (A) going into finer spatial and temporal scales, and (B) conditioning the surface weather generator on larger scale circulation simulated by circulation weather generator run at much coarser resolution. The development of the circulation generator (CIRCULATOR) has started in 2019. It is based on the first-order multivariate autoregressive model (similar to the one used in SPAGETTA), and the set of generator’s variables consists of larger scale characteristics of atmospheric circulation (derived from the NCEP/NCAR reanalysis), temperature and precipitation defined for a 2.5 degree grid. In our contribution, we will show results related to these two activities, focusing on (i) WG’s ability to reproduce spatial temperature-precipitation spells at various spatial scales (down to EUR-11 resolution) for eight European regions, (ii) validation of the circulation generator in terms of its ability to reproduce frequencies of circulation patterns and larger-scale temperature and precipitation characteristics for the 8 regions, and (iii) assessing an effect of using the circulation generator to drive the surface weather generator on its ability to reproduce the compound spells.</p><p> </p><p>Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).</p>

Visualization and quantification of NDVI dynamics in semiarid grasslands through recurrence tools.

2021 ◽  
Author(s):  
Andrés F. Almeida-Ñauñay ◽  
Ernesto Sanz ◽  
Miguel Quemada ◽  
Juan C. Losada ◽  
Rosa M. Benito ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Dynamics ◽  
Climate System ◽  
Recurrence Plots ◽  
Time Step ◽  
Temporal Scales ◽  
Temporal Behaviour ◽  
Semiarid Grassland ◽  
Temperature And Precipitation ◽  
Ndvi Time Series

<p>Grassland dynamics are constantly changing at a variety of spatial and temporal scales. Remote-sensing techniques are used to detect, identify, and monitor ecosystem changes at multi-temporal scales. Particularly, Normalized Difference Vegetation Index (NDVI)-based time series are important to obtain numerical observations related to vegetation dynamics.</p> <p>It is within this context that Recurrence Plots (RPs), Cross Recurrence Plots (CRPs) and Recurrence Quantification Analysis (RQA) offer new insight into the analysis of non-linear processes. Altogether, recurrence techniques could describe the whole dynamics of the system, explore its temporal behaviour, and quantify its structure through complexity measures. The goal of this study is to compute recurrence techniques to visualize and quantify the temporal dynamics of the semiarid grassland-climate system.</p> <p>In this work, we chose a semiarid grassland area in the centre of Spain, characterized by a Mediterranean climate. Multispectral images were composed for 8-days and they were acquired from MODIS TERRA (MOD09Q1.006) product from 2002 to 2018. Then, NDVI time-series was generated from four pixels with a spatial resolution of 250 x 250 m<sup>2</sup>. Temperature and precipitation time-series were obtained from a nearby meteorological station and transformed into an 8-day time step.</p> <p>Our results demonstrated that RPs showed the seasonality of the NDVI time-series. Furthermore, abrupt changes in NDVI time series were detected at specific times, implying that an atypical event occurred during that time. Temperature-NDVI CRPs showed a periodical pattern between them, on the other hand, precipitation-NDVI CRPs showed more erratic behaviour. We also found that a maximum lag between the two time-series could be detected through recurrence techniques. Overall, our findings suggest that temperature and precipitation present a dynamic complexity that is revealed in NDVI response. Therefore, RPs and CRPs are an alternative and complementary method to analyse and quantify non-stationary process, such as vegetation dynamics.</p> <p><strong>Reference</strong></p> <p>Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. Complexity of the Vegetation-Climate System Through Data Analysis. In International Conference on Complex Networks and Their Applications. Springer, Cham., 609-619, 2020</p> <p><strong>Acknowledgements</strong></p> <p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p> <p> </p>

scholarly journals ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models

2017 ◽  
Vol 10 (1) ◽  
pp. 57-83 ◽  
Author(s):  
Nicholas P. Klingaman ◽  
Gill M. Martin ◽  
Aurel Moise
Keyword(s):  
Temporal Variability ◽  
General Circulation ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Time Step ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Tropical Precipitation ◽  
Random Behavior ◽  
The Tropics

Abstract. General circulation models (GCMs) have been criticized for their failure to represent the observed scales of precipitation, particularly in the tropics where simulated daily rainfall is too light, too frequent and too persistent. Previous assessments have focused on temporally or spatially averaged precipitation, such as daily means or regional averages. These evaluations offer little actionable information for model developers, because the interactions between the resolved dynamics and parameterized physics that produce precipitation occur at the native gridscale and time step. We introduce a set of diagnostics (Analyzing Scales of Precipitation, version 1.0 – ASoP1) to compare the spatial and temporal scales of precipitation across GCMs and observations, which can be applied to data ranging from the gridscale and time step to regional and sub-monthly averages. ASoP1 measures the spectrum of precipitation intensity, temporal variability as a function of intensity and spatial and temporal coherence. When applied to time step, gridscale tropical precipitation from 10 GCMs, the diagnostics reveal that, far from the dreary persistent light rainfall implied by daily mean data, most models produce a broad range of time step intensities that span 1–100 mm day−1. Models show widely varying spatial and temporal scales of time step precipitation. Several GCMs show concerning quasi-random behavior that may influence and/or alter the spectrum of atmospheric waves. Averaging precipitation to a common spatial ( ≈  600 km) or temporal (3 h) resolution substantially reduces variability among models, demonstrating that averaging hides a wealth of information about intrinsic model behavior. When compared against satellite-derived analyses at these scales, all models produce features that are too large and too persistent.

scholarly journals The European 2015 drought from a hydrological perspective

2016 ◽  
Author(s):  
Gregor Laaha ◽  
Tobias Gauster ◽  
Lena M. Tallaksen ◽  
Jean-Philippe Vidal ◽  
Kerstin Stahl ◽  
...  
Keyword(s):  
Water Cycle ◽  
Flow Characteristics ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Low Flow ◽  
Drought Indices ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Antecedent Conditions ◽  
Temperature And Precipitation

Abstract. In 2015 large parts of Europe were affected by a drought. In two companion papers we summarize a collaborative initiative of members of UNESCO’s EURO FRIEND-Water program to perform a timely pan-European assessment of the event. In this second paper, we analyse the event of 2015 relative to the event of 2003 based on streamflow observations. Analyses are based on range of low flow and hydrological drought indices for about 800 records across Europe that were collected in a community effort based on a common protocol. We compare the hydrological footprints of both events with the meteorological footprints presented by Ionita et al. (2016), in order to learn from similarities and differences of both perspectives and to draw conclusions for drought management. Overall, the hydrological drought of 2015 is characterised by a different spatial extent than the drought of 2003. In terms of low flow magnitude, a region around the Czech Republic was most affected with annual low flows that exhibited return intervals of 100 years and more. In terms of deficit volumes, the geographical centre of the event was in the area of Southern Germany where the drought lasted particularly long. A detailed assessment at various spatial and temporal scales showed that the different behaviour in these regions was also a result of diverging wetness preconditions in the catchments. Extreme droughts emerged where antecedent conditions were particularly dry. In regions with wet preconditions, low flow events developed later, and were mostly less severe. The space-time patterns of monthly low flow characteristics show that meteorological and hydrological events spread differently across Europe, and they evolved differently in regard to extent and severity. The results underline that drought is a hazard that leaves different footprints on the various components of the water cycle, on different spatial and temporal scales. The different dynamic development of major hydrometeorological characteristics, temperature and precipitation anomalies versus the streamflow magnitude, duration and deficit volume also determine differences in the impacts of hydrological drought on various water use sectors and on river ecology. For an assessment of drought impacts on water resources, therefore, hydrological data is required in addition to the hydro-meteorological drought indices. Additional efforts with a pan-European dimension need to be undertaken to make timely hydrological assessments more operational in the future.

scholarly journals Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogs

2019 ◽  
Author(s):  
Damien Raynaud ◽  
Benoit Hingray ◽  
Guillaume Evin ◽  
Anne-Catherine Favre ◽  
Jérémy Chardon
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Weather Generator ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Natural Risk ◽  
Synoptic Weather ◽  
Weather Generators ◽  
Long Time ◽  
Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorology records used to characterize the large-scale atmospheric configuration of the generation day. To overcome those limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days in the 20th century to generate a 1000-year sequence of new atmospheric trajectories and (2) a stochastic downscaling model in a second step, applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analog-based weather generators.

scholarly journals Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues

2020 ◽  
Vol 24 (9) ◽  
pp. 4339-4352
Author(s):  
Damien Raynaud ◽  
Benoit Hingray ◽  
Guillaume Evin ◽  
Anne-Catherine Favre ◽  
Jérémy Chardon
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Weather Generator ◽  
Weather Variables ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Natural Risk ◽  
Synoptic Weather ◽  
Weather Generators ◽  
Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

scholarly journals Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

2016 ◽  
Author(s):  
Gill M. Martin ◽  
Nicholas P. Klingaman ◽  
Aurel F. Moise
Keyword(s):  
Spatial Scales ◽  
Rainfall Variability ◽  
Deep Convection ◽  
Daily Rainfall ◽  
Step Length ◽  
Tropical Convection ◽  
Time Step ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Short Scale

Abstract. This study analyses tropical rainfall variability, on a range of temporal and spatial scales, in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, compared with two satellite-derived rainfall datasets. We focus on the shorter scales i.e. from the native grid and time-step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time-step is largely independent of the grid-box size and time-step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time-step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid-boxes/time-steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ~ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ~ 10 day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling the small/short scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of the small/short scale processes.

scholarly journals Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

2017 ◽  
Vol 10 (1) ◽  
pp. 105-126 ◽  
Author(s):  
Gill M. Martin ◽  
Nicholas P. Klingaman ◽  
Aurel F. Moise
Keyword(s):  
Spatial Scales ◽  
Rainfall Variability ◽  
Deep Convection ◽  
Daily Rainfall ◽  
Step Length ◽  
Tropical Convection ◽  
Time Step ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Short Scale

Abstract. This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellite-derived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the  ∼  100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at  ∼  10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

scholarly journals A Constrained Stochastic Weather Generator for Daily Mean Air Temperature and Precipitation

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Feifei Pan ◽  
Lisa Nagaoka ◽  
Steve Wolverton ◽  
Samuel F. Atkinson ◽  
Timothy A. Kohler ◽  
...  
Keyword(s):  
Air Temperature ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Weather Conditions ◽  
Weather Generator ◽  
Total Precipitation ◽  
Sinusoidal Wave ◽  
Stochastic Weather Generator ◽  
Temperature And Precipitation ◽  
Unique Method

A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.

scholarly journals Application of spatial weather generator for the assessment of climate change impacts on a river runoff

Geografie ◽  
2014 ◽  
Vol 119 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Leszek Kuchar ◽  
Sławomir Iwański ◽  
Leszek Jelonek ◽  
Wiwiana Szalińska
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Change Impacts ◽  
Weather Generator ◽  
Time Step ◽  
Weather Patterns ◽  
Global Circulation Models ◽  
Temperature And Precipitation ◽  
Projected Changes ◽  
Daily Time

In this study, the impacts of climate change on streamflow are investigated. The ensemble of outputs from three different Global Circulation Models models: GISS, CCCM, GFDL developed for the emission scenario A1B were analyzed to infer projected changes in climatological conditions for the region of the Upper and Middle Odra basin. Obtaining hydrological scenarios of future changes for the scale of subcatchment required simulating short-term and fine scaled weather patterns for this area. SWGEN model (Spatial Weather GENerator) was applied to downscale projected changes of climatological conditions to the ones required by hydrological model temporal and spatial resolution. Daily time series of solar radiation, temperature and precipitation were generated for the reference period 1981–2000 and for the time horizon 2030 and 2050. The generated data from SWGEN model were integrated in the hydrological model NAM to simulate streamflow under changed conditions with daily time step. The results show considerable changes in annual and seasonal runoff daily distributions for selected study catchment in the future time horizons of 2030 and 2050.

Sign in / Sign up

Export Citation Format

Share Document