scholarly journals Control Simulation Experiment with the Lorenz’s Butterfly Attractor

2021 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Qiwen Sun
Keyword(s):  
Simulation Experiment ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Effective Control ◽  
Observation Error ◽  
Small Perturbations ◽  
Control Simulation ◽  
Starting Point ◽  
Nwp Model ◽  
Systems Simulation

Abstract. In numerical weather prediction (NWP), the sensitivity to initial conditions brings chaotic behaviors and an intrinsic limit to predictability, but it also implies an effective control in which a small control signal grows rapidly to make a substantial difference. The Observing Systems Simulation Experiment (OSSE) is a well-known approach to study predictability, where “the nature” is synthesized by an independent NWP model run. In this study, we extend the OSSE and design the control simulation experiment (CSE) where we apply a small signal to control “the nature”. Idealized experiments with the Lorenz-63 three-variable system show that we can control “the nature” to stay in a chosen regime without shifting to the other, i.e., in a chosen wing of the Lorenz’s butterfly attractor, by adding small perturbations to “the nature”. Using longer-lead-time forecasts, we achieve more effective control with a perturbation size less than only 3 % of the observation error. We anticipate our idealized CSE to be a starting point for realistic CSE using the real-world NWP systems, toward possible future applications to reduce weather disaster risks. The CSE may be applied to other chaotic systems beyond NWP.

Reexamining the tropical Atlantic influence on ENSO using perfect model predictability experiments

2021 ◽  
Author(s):  
Ingo Richter ◽  
Yu Kosaka ◽  
Hiroki Tokinaga ◽  
Shoichiro Kido
Keyword(s):  
Initial Conditions ◽  
Boreal Summer ◽  
Similar Amount ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
Boreal Spring ◽  
Modes Of Variability ◽  
Control Simulation ◽  
Starting Point ◽  
Perfect Model

<p>The potential influence of the tropical Atlantic on the development of ENSO has received increased attention over recent years. In particular equatorial Atlantic variability (also known as the Atlantic zonal mode or AZM) has been shown to be anticorrelated with ENSO, i.e. cold AZM events in boreal summer (JJA) tend to be followed by El Niño in winter (DJF), and vice versa for warm AZM events. One problem with disentangling the two-way interaction between the equatorial Atlantic and Pacific is that both ENSO and the AZM tend to develop in boreal spring (MAM).</p><p>Here we use a set of GCM sensitivity experiments to quantify the strength of the Atlantic-Pacific link. The starting point is a 1000-year free-running control simulation with the GFDL CM 2.1 model. From this control simulation, we pick years in which a cold AZM event in JJA is followed by an El Niño in DJF. These years serve as initial conditions for “perfect model” prediction experiments with 10 ensemble members each. In the control experiments, the predictions evolve freely for 12 months from January 1 of each selected year. In the second set of predictions, SSTs are gradually relaxed to climatology in the tropical Atlantic, so that the cold AZM event is suppressed. In the third set of predictions, we restore the tropical Pacific SSTs to climatology, so that the El Niño event is suppressed.</p><p>The results suggest that, on average, the tropical Atlantic SST anomalies increase the strength of El Niño in the following winter by about 10-20%. If, on the other hand, El Niño development is suppressed, the amplitude of the cold AZM event also reduces by a similar amount. The results suggest that, in the context of this GCM, the influence of AZM events on ENSO development is relatively weak but not negligible. The fact that ENSO also influences the AZM in boreal spring highlights the complex two-way interaction between these two modes of variability.</p>

scholarly journals Asymmetricity of ground-based GPS slant delay data

2007 ◽  
Vol 7 (12) ◽  
pp. 3143-3151 ◽  
Author(s):  
R. Eresmaa ◽  
H. Järvinen ◽  
S. Niemelä ◽  
K. Salonen
Keyword(s):  
Standard Deviation ◽  
Global Positioning System ◽  
Weather Prediction ◽  
Small Scale ◽  
Tropospheric Delay ◽  
Observation Error ◽  
Positioning System ◽  
Global Positioning ◽  
Nwp Model ◽  
Delay Component

Abstract. The ground-based measurements of the Global Positioning System (GPS) allow estimation of the tropospheric delay along the slanted signal paths through the atmosphere. The meteorological exploitation of such slant delay (SD) observations relies on the hypothesis of azimuthal asymmetry of the information content. This article addresses the validity of the hypothesis. A new concept of asymmetricity is introduced for studying the SD observations and their model counterparts. The asymmetricity is defined as the ratio of the absolute asymmetric delay component to total SD. The model counterparts are determined from 3-h forecasts of a numerical weather prediction (NWP) model, run with four different horizontal resolutions. The SD observations are compared with their model counterparts with emphasis on cases of high asymmetricity in order to see whether the observed asymmetry is a real atmospheric signature. The asymmetricity is found to be of the order of a few parts per thousand. Thus, the asymmetric delay component barely exceeds the assumed standard deviation of the SD observation error. However, the observed asymmetric delay components show a statistically significant meteorological signal. Benefit of the asymmetric SD observations is therefore expected to be taken in future, when NWP systems will explicitly represent the small-scale atmospheric features revealed by the SD observations.

scholarly journals Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

2021 ◽  
Author(s):  
Matthias Aichinger-Rosenberger ◽  
Natalia Hanna ◽  
Robert Weber
Keyword(s):  
Numerical Weather Prediction ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Global Navigation Satellite Systems ◽  
Single Frequency ◽  
Tropospheric Delay ◽  
Global Networks ◽  
Numerical Weather ◽  
Station Coordinates ◽  
Nwp Model

<p>Electromagnetic signals, as broadcasted by Global Navigation Satellite Systems (GNSS), are delayed when travelling through the Earth’s atmosphere due to the presence of water vapour. Parametrisations of this delay, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially the Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g. station coordinates). Results are typically used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA).</p><p>This contribution goes beyond the approach outlined above as it shows how reasonable tropospheric parameters can be derived from highly kinematic, single-frequency (SF) GNSS data. The utilized data was gathered at trains by the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. Although the special nature of the observations yields several challenges concerning data processing, we show that reasonable results for ZTD estimates can be obtained for all four analysed test cases by using different PPP processing strategies. Comparison with ZTD calculated from ERA5 reanalysis data yields a very high correlation and an overall agreement from the low millimetre-range up to 5 cm, depending on solution and analysed travelling track. We also present the first tests of assimilation into a numerical weather prediction (NWP) model which show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore, we investigate means to transfer the developed ideas to an operational stage in order to exploit the huge benefits (horizontal/temporal resolution) of this special dataset for operational weather forecasting. </p>

scholarly journals Azimuthal asymmetry in ground-based GPS slant delay observations and their NWP model counterparts

2007 ◽  
Vol 7 (1) ◽  
pp. 3179-3202 ◽  
Author(s):  
R. Eresmaa ◽  
H. Järvinen ◽  
S. Niemelä ◽  
K. Salonen
Keyword(s):  
Global Positioning System ◽  
Weather Prediction ◽  
Azimuthal Asymmetry ◽  
Small Scale ◽  
Tropospheric Delay ◽  
Observation Error ◽  
Global Positioning ◽  
Nwp Model ◽  
Delay Component ◽  
High Asymmetry

Abstract. The ground-based measurements of the Global Positioning System (GPS) allow estimation of the tropospheric delay along the slanted signal paths through the atmosphere. The meteorological exploitation of such slant delay (SD) observations relies on the hypothesis of azimuthal asymmetry of the information content. This article addresses the validity of the hypothesis. The asymmetric properties of the SD observations and their model counterparts are investigated. In this study, the model counterparts are based on 3-h forecasts of a numerical weather prediction (NWP) model, run with four different horizontal resolutions. The SD observations are compared with their model counterparts with emphasis on cases of high asymmetry in order to see whether the observed asymmetry is a real atmospheric signature. The asymmetric delay component is found to be of the order of a few parts per thousand of the absolute SD value, thus barely exceeding the assumed standard deviation of the SD observation error. However, the observed asymmetric delay components show a statistically significant meteorological signal. Benefit of the asymmetric SD observations is therefore expected to be taken in future, when NWP systems will explicitly represent the small-scale atmospheric features revealed by the SD observations.

scholarly journals Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

2019 ◽  
Vol 23 (1) ◽  
pp. 493-513 ◽  
Author(s):  
Samuel Monhart ◽  
Massimiliano Zappa ◽  
Christoph Spirig ◽  
Christoph Schär ◽  
Konrad Bogner
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Added Value ◽  
Ensemble Prediction ◽  
Late Winter ◽  
Streamflow Prediction ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Temperature And Precipitation ◽  
Nwp Model

Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.

Regime-dependent statistical post-processing of ensemble forecasts

2020 ◽  
Author(s):  
Sam Allen ◽  
Christopher Ferro ◽  
Frank Kwasniok
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Forecast ◽  
State Variables ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Nwp Model ◽  
Processing Techniques ◽  
Geographical Locations

<p>A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post-processing. Post-processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz ‘96 system, ensemble forecasts are subjected to well-known post-processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post-processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.</p>

scholarly journals Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Wansik Yu ◽  
Eiichi Nakakita ◽  
Sunmin Kim ◽  
Kosei Yamaguchi
Keyword(s):  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Uncertainty Propagation ◽  
Precipitation Forecast ◽  
Flood Forecast ◽  
Catchment Scale ◽  
Forecast Uncertainty ◽  
Specific Discharge ◽  
Nwp Model

The common approach to quantifying the precipitation forecast uncertainty is ensemble simulations where a numerical weather prediction (NWP) model is run for a number of cases with slightly different initial conditions. In practice, the spread of ensemble members in terms of flood discharge is used as a measure of forecast uncertainty due to uncertain precipitation forecasts. This study presents the uncertainty propagation of rainfall forecast into hydrological response with catchment scale through distributed rainfall-runoff modeling based on the forecasted ensemble rainfall of NWP model. At first, forecast rainfall error based on the BIAS is compared with flood forecast error to assess the error propagation. Second, the variability of flood forecast uncertainty according to catchment scale is discussed using ensemble spread. Then we also assess the flood forecast uncertainty with catchment scale using an estimation regression equation between ensemble rainfall BIAS and discharge BIAS. Finally, the flood forecast uncertainty with RMSE using specific discharge in catchment scale is discussed. Our study is carried out and verified using the largest flood event by typhoon “Talas” of 2011 over the 33 subcatchments of Shingu river basin (2,360 km2), which is located in the Kii Peninsula, Japan.

scholarly journals Evaluating the use of Aeolus satellite observations in the regional NWP model Harmonie-Arome

2021 ◽  
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Line Of Sight ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

Abstract. The impact of using wind speed data from the Aeolus satellite in a limited area Numerical Weather Prediction (NWP) system is being investigated using the limited area NWP model Harmonie-Arome over the Nordic region. We assimilate the Horizontal Line of Sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in Sept–Oct 2018 when the satellite was recently launched, and a later period in Apr–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations have degraded between the first and second experiment period over our domain. However observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generate flow-dependent analysis increments, with the Aeolus data.

scholarly journals Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

2021 ◽  
Vol 14 (9) ◽  
pp. 5925-5938
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Numerical Weather ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

Abstract. The impact of using wind observations from the Aeolus satellite in a limited-area numerical weather prediction (NWP) system is being investigated using the limited-area NWP model Harmonie–Arome over the Nordic region. We assimilate the horizontal line-of-sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second experiment period over our domain. However, observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods, whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generates flow-dependent analysis increments, with the Aeolus data.

scholarly journals Assimilating visible satellite reflectances in combination with radar data in a pre-operational convective-scale seamless prediction system 

2021 ◽  
Author(s):  
Liselotte Bach ◽  
Thomas Deppisch ◽  
Leonhard Scheck ◽  
Alberto de Lozar ◽  
Christian Welzbacher ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Weather Prediction ◽  
Radar Data ◽  
Observation Error ◽  
Major Step ◽  
Observation Systems ◽  
Seamless Transition ◽  
Convective Scale ◽  
Nwp Model

<p>In the framework of the SINFONY project at Deutscher Wetterdienst (DWD) we have developed data assimilation of visible satellite reflectances of the SEVIRI instrument (MSG) and radar observations in a rapid update cycle (ICON-D2-KENDA-RUC) which will be running in a first 24/7-testsuite starting in spring of this year. Our major goal related to the assimilation of these new observation systems is to improve the positioning of cloud and precipitation systems and their intensities, needed for the seamless transition of radar nowcasting to numerical weather prediction (NWP) in our SINFONY system. We give an overview of the steps undertaken in the course of developing the data assimilation of visible satellite reflectances. This includes quality control, observation error modelling, data reduction and bias correction of the reflectances. Further development and enhancement of the forward operator MFASIS is still ongoing. A major step to allow for a successful assimilation has been the improvement of microphysical consistency between the NWP model and MFASIS both with 1-moment and 2-moment microphysics to reduce the bias of first-guess departures. To further enhance and stabilize the agreement of observations and model climatologies over the course of the year and different weather regimes, an innovative histogram-based bias correction has been developed. We show results of data assimilation experiments combining visible reflectances and radar data in the ICON-D2-KENDA-Rapid Update Cycle using 2-moment microphysics. Further, we discuss the improvement of forecast skill from both observing systems and the way they complement each other – putting special emphasis to the key variable of interest in the SINFONY system, namely radar reflectivity.</p>

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