Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation

Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.

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Advances in the Study of Severe Convection Weather Nowcasting in Central China

10.5194/egusphere-egu2020-1656 ◽

2020 ◽

Author(s):

Chunguang Cui ◽

Yanjiao Xiao ◽

Anwei Lai ◽

Muyun Du

Keyword(s):

Weather Forecasting ◽

Initial Conditions ◽

Weather Prediction ◽

Central China ◽

Dual Polarization ◽

Specific Difference ◽

Severe Convection ◽

Rainfall Forecast ◽

Short Time ◽

Differential Reflectivity

Based on the characteristics of sudden and local, short life history, serious disasters and so on, the severe convection weather system is difficult to be captured by the conventional meteorological observation network, and is still challenging for catastrophic weather forecasting. In order to improve the service ability in strong weather monitoring and prediction, the following researches have been carried out recently: (1) The new mesocyclone and tornado vortex feature recognition algorithms are developed and proved to be successfully in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, a number of parameters (exceed thirty) have been used in the research on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time strong precipitation). Based on large sample data and results of a variety of analysis methods, a thunderstorm winds Bayes discriminant model has also been established. The testing results show that its Heidke skill score is 0.836, along with the accuracy rate and hit rate are greater than 95%, and the empty rate is below 5%. (2) Rapid update cycle forecast system can effectively improve the quality of model initial values that is very suitable for short time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. In addition, a new set of simplified and parameterized dual-polarization radar simulators for horizontal reflectivity (ZH), differential reflectivity (ZDR), specific difference phase (KDP), and correlation coefficient (&#961;HV) have been co-developed, and some preliminary data assimilation experiments have shown that the assimilation of dual polarization variables including differential reflectivity and specific difference phase in addition to radar radial velocity and horizontal reflectivity can help improve the accuracy of initial conditions for model hydrometer variables and ensuing model forecasts. (3) Although not yet mature enough for meteorological application, blending technology which is expected to overcome the deficiency of the quantitative precipitation (QPF) by a mesoscale NWP model for the short term at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours is under development and debugging, and also has potential in enhancing the ability of rainfall forecast within the nowcasting period. (4) The above methods and systems were applied and provided technical support for meteorological services during the 7th Wuhan World Military Games in 2019, and a good service effect had been achieved.

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Shallow Cumulus in WRF Parameterizations Evaluated against LASSO Large-Eddy Simulations

Monthly Weather Review ◽

10.1175/mwr-d-18-0115.1 ◽

2018 ◽

Vol 146 (12) ◽

pp. 4303-4322 ◽

Cited By ~ 16

Author(s):

Wayne M. Angevine ◽

Joseph Olson ◽

Jaymes Kenyon ◽

William I. Gustafson ◽

Satoshi Endo ◽

...

Keyword(s):

Mass Flux ◽

Prediction Models ◽

Initial Conditions ◽

Weather Prediction ◽

Cloud Amount ◽

Large Eddy Simulations ◽

Shallow Cumulus ◽

Wide Range ◽

Large Eddy ◽

Single Column

AbstractRepresentation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.

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Effects of Initial and Boundary Conditions of Mesoscale Models on Simulated Atmospheric Refractivity

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-012.1 ◽

2012 ◽

Vol 51 (1) ◽

pp. 115-132 ◽

Cited By ~ 14

Author(s):

Changgui Wang ◽

Damian Wilson ◽

Tracy Haack ◽

Peter Clark ◽

Humphrey Lean ◽

...

Keyword(s):

Boundary Layer ◽

Initial Conditions ◽

Weather Prediction ◽

Horizontal Resolution ◽

Atmospheric Environment ◽

Initial State ◽

Weather Patterns ◽

Sst Variability ◽

Underlying Surface Conditions ◽

Nwp Model

AbstractRadar ducting is caused by sharp vertical changes in temperature and, especially, water vapor at the top of the atmospheric boundary layer, both of which are sensitive to variations in the underlying surface conditions, local mesoscale weather, and synoptic weather patterns. High-resolution numerical weather prediction (NWP) models offer an alternative to observation to determine boundary layer (BL) structure and to assess the spatial variability of radar ducts. The benefit of using NWP models for simulating ducting conditions very much depends on the initial state of sea surface temperature (SST) and the model spinup time, both of which have a great impact on BL structure. This study investigates the effects of variation of NWP-model initial conditions and simulation length on the accuracy of simulating the atmosphere’s refractive index over the Wallops Island, Virginia, region, which has pronounced SST variability and complex BL structure. The Met Office Unified Model (MetUM) with horizontal resolution of 4 km (4-km model) was used to simulate the atmospheric environment when observations were made during the Wallops-2000 experiment. Sensitivity tests were conducted in terms of the variability of SST and model spinup time. The evaluation of the model results was focused on low-level moisture, temperature, and surface ducting characteristics including the frequency, strength, and the height of the ducting layer. When provided with more accurate SST and adequate simulation length, the MetUM 4-km model demonstrated an improved ability to predict the observed ducting.

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Neuro-Inspired Computing with Spin-VCSELs

Applied Sciences ◽

10.3390/app11094232 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4232

Author(s):

Krishan Harkhoe ◽

Guy Verschaffelt ◽

Guy Van der Sande

Keyword(s):

Processing Speed ◽

Delay Time ◽

High Speed ◽

State Of The Art ◽

Polarization Modulation ◽

Time Interval ◽

Computing Technique ◽

High Modulation ◽

Short Time ◽

Modulation Speed

Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.

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Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-3581-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 3581-3610

Author(s):

S. Federico

Keyword(s):

Relative Humidity ◽

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Wind Profiler ◽

Short Term ◽

Recursive Filters ◽

Data Assimilation System ◽

Rams Model ◽

Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

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Application of Adjoint-Derived Forecast Sensitivities to the 24–25 January 2000 U.S. East Coast Snowstorm

Monthly Weather Review ◽

10.1175/mwr3023.1 ◽

2005 ◽

Vol 133 (11) ◽

pp. 3148-3175 ◽

Cited By ~ 20

Author(s):

Daryl T. Kleist ◽

Michael C. Morgan

Keyword(s):

Response Function ◽

Initial Conditions ◽

Forecast Error ◽

Weather Prediction ◽

Lower Troposphere ◽

Vertical Motion ◽

Initial Condition ◽

Response Functions ◽

Eastern United States ◽

Surface Cyclone

Abstract The 24–25 January 2000 eastern United States snowstorm was noteworthy as operational numerical weather prediction (NWP) guidance was poor for lead times as short as 36 h. Despite improvements in the forecast of the surface cyclone position and intensity at 1200 UTC 25 January 2000 with decreasing lead time, NWP guidance placed the westward extent of the midtropospheric, frontogenetically forced precipitation shield too far to the east. To assess the influence of initial condition uncertainties on the forecast of this event, an adjoint model is used to evaluate forecast sensitivities for 36- and 48-h forecasts valid at 1200 UTC 25 January 2000 using as response functions the energy-weighted forecast error, lower-tropospheric circulation about a box surrounding the surface cyclone, 750-hPa frontogenesis, and vertical motion. The sensitivities with respect to the initial conditions for these response functions are in general very similar: geographically isolated, maximized in the middle and lower troposphere, and possessing an upshear vertical tilt. The sensitivities are maximized in a region of enhanced low-level baroclinicity in the vicinity of the surface cyclone’s precursor upper trough. However, differences in the phase and structure of the gradients for the four response functions are evident, which suggests that perturbations could be constructed to alter one response function but not necessarily the others. Gradients of the forecast error response function with respect to the initial conditions are used in an iterative procedure to construct initial condition perturbations that reduce the forecast error. These initial condition perturbations were small in terms of both spatial scale and magnitude. Those initial condition perturbations that were confined primarily to the midtroposphere grew rapidly into much larger amplitude upper-and-lower tropospheric perturbations. The perturbed forecasts were not only characterized by reduced final time forecast error, but also had a synoptic evolution that more closely followed analyses and observations.

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Model error in weather forecasting

Nonlinear Processes in Geophysics ◽

10.5194/npg-8-357-2001 ◽

2001 ◽

Vol 8 (6) ◽

pp. 357-371 ◽

Cited By ~ 90

Author(s):

D. Orrell ◽

L. Smith ◽

J. Barkmeijer ◽

T. N. Palmer

Keyword(s):

Prediction Models ◽

Weather Forecasting ◽

Initial Conditions ◽

Forecast Error ◽

Weather Prediction ◽

Model Error ◽

Local Model ◽

State Dependent ◽

Rapid Divergence ◽

Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

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Flash-flood early warning using weather radar data: from nowcasting to forecasting

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-1289-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 1289-1331 ◽

Cited By ~ 3

Author(s):

K. Liechti ◽

L. Panziera ◽

U. Germann ◽

M. Zappa

Keyword(s):

Early Warning ◽

Initial Conditions ◽

Flash Flood ◽

Weather Prediction ◽

Probabilistic Approach ◽

Radar Data ◽

Swiss Alps ◽

Probabilistic Forecasting ◽

Orographic Rainfall ◽

Forecasting System

Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel probabilistic radar-based forecasting chains for flash-flood early warning are investigated in three catchments in the Southern Swiss Alps and set in relation to deterministic discharge forecast for the same catchments. The first probabilistic radar-based forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second probabilistic forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialized with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. We found a clear preference for the probabilistic approach. Discharge forecasts perform better when forced by NORA rather than by a persistent radar QPE for lead times up to eight hours and for all discharge thresholds analysed. The best results were, however, obtained with the REAL-C2 forecasting chain, which was also remarkably skilful even with the highest thresholds. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

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On the Sensitivity of the Simulated Diurnal Cycle of Precipitation to 3-Hourly Radiosonde Assimilation: A Case Study over the Western Maritime Continent

Monthly Weather Review ◽

10.1175/mwr-d-20-0423.1 ◽

2021 ◽

Vol 149 (10) ◽

pp. 3449-3468

Author(s):

Joshua Chun Kwang Lee ◽

Anurag Dipankar ◽

Xiang-Yu Huang

Keyword(s):

Diurnal Cycle ◽

High Frequency ◽

Initial Conditions ◽

Rainfall Variability ◽

Weather Prediction ◽

Radiosonde Data ◽

Western Coast ◽

Maritime Continent ◽

Model Biases ◽

Diurnal Cycle Of Precipitation

AbstractThe diurnal cycle is the most prominent mode of rainfall variability in the tropics, governed mainly by the strong solar heating and land–sea interactions that trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of the additional wind, temperature, and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high-frequency in situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation, and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight how during the assimilation, certain multivariate background error constraints and moisture addition in an ad hoc manner can negatively impact the simulations. Other approaches should be explored to better exploit information from such high-frequency observations over this region.

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Role of initial condition and parametric uncertainty in a severe hailstorm forecast

10.5194/egusphere-egu21-7406 ◽

2021 ◽

Author(s):

Patrick Kuntze ◽

Annette Miltenberger ◽

Corinna Hoose ◽

Michael Kunz

Keyword(s):

Initial Conditions ◽

Weather Prediction ◽

Parametric Uncertainty ◽

Radar Data ◽

Cloud Microphysics ◽

Initial Condition ◽

Relative Contribution ◽

Cloud Properties ◽

Set Up ◽

The Impact

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany. To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation. In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.

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