A generalized approach to generate synthetic short-to-medium range hydro-meteorological forecasts

2021 ◽  
Author(s):  
Zachary Brodeur ◽  
Scott Steinschneider
Keyword(s):  
Water Resources ◽  
Case Studies ◽  
Weather Prediction ◽  
Flood Management ◽  
Great Promise ◽  
Forecast Errors ◽  
Computationally Efficient ◽  
Empirical Copula ◽  
Temperature And Precipitation ◽  
Medium Range

<p>Forecast informed operations hold great promise as a soft pathway to improve water resources system performance. Generating synthetic forecasts of hydro-meteorological variables is crucial for robust validation of this approach, as advanced numerical weather prediction hindcasts have a limited timespan (10-40 years) that is insufficient for assessing risk related to forecast-informed operations during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the flexible Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables and forecast lead times and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from operational CONUS hydrologic and meteorological forecast models. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for flood management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for the design and testing of forecast informed policy or characterization of forecast uncertainty for water resources risk analysis.</p>

scholarly journals Potential vorticity dynamics of life cycles of blocked weather regimes in the Euro-Atlantic region: A diagnostic framework

2021 ◽  
Author(s):  
Seraphine Hauser ◽  
Christian M. Grams ◽  
Michael Riemer ◽  
Peter Knippertz ◽  
Franziska Teubler
Keyword(s):  
Potential Vorticity ◽  
Large Scale ◽  
Climate Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Life Cycles ◽  
Forecast Errors ◽  
Atlantic Region ◽  
Weather Regimes ◽  
Medium Range

<p>Quasi-stationary, persistent, and recurrent states of the large-scale extratropical circulation, so-called weather regimes, characterize the atmospheric variability on sub-seasonal timescales of several days to a few weeks. Weather regimes featuring a blocking anticyclone are of particular interest due to their long lifetime and potential for high-impact weather. However, state-of-the-art numerical weather prediction and climate models struggle to correctly represent blocking life cycles, which results in large forecast errors at the medium-range to sub-seasonal timescale. Despite progress in recent years, we are still lacking a process-based conceptual understanding of blocked regime dynamics, which hinders a better representation of blocks in numerical models. In particular the relative contributions of dry and moist processes in the onset and maintenance of a block remain unclear.</p><p>Here we aim to revisit the dynamics of blocking in the Euro-Atlantic region. To this end we investigate the life cycles of blocked weather regimes from a potential vorticity (PV) perspective in ERA5 reanalysis data (from 1979 to present) from the European Centre for Medium-Range Weather Forecasts. We develop a diagnostic PV framework that allows the tracking of negative PV anomalies associated with blocked weather regimes. Complemented by piecewise PV-tendencies - separated into advective and diabatic PV tendencies - we are able to disentangle different physical processes affecting the amplitude evolution of negative PV anomalies associated with blocked regimes. Most importantly, this approach newly enables us to distinguish between the roles of dry and moist dynamics in the initiation and maintenance of blocked weather regimes in a common framework. A first application demonstrates the functionality of the developed PV framework and corroborates the importance of moist-diabatic processes in the initiation and maintenance of a block in a regime life cycle. </p>

The role of arctic forecast errors in the evolution of northern extra-tropical forecast skill

2020 ◽  
Author(s):  
Thomas Haiden
Keyword(s):  
Forecast Error ◽  
Weather Prediction ◽  
Polar Vortex ◽  
Forecast Skill ◽  
The Arctic ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Medium Range Forecast ◽  
Medium Range

<p><br>Increases in extra-tropical numerical weather prediction (NWP) skill over the last decades have been well documented. The role of the Arctic, defined here as the area north of 60N, in driving (or slowing) this improvement has however not been systematically assessed. To investigate this question, spatial patterns of changes in medium-range forecast error of ECMWF’s Integrated Forecast System (IFS) are analysed both for deterministic and ensemble forecasts. The robustness of these patterns is evaluated by comparing results for different parameters and levels, and by comparing them with the respective changes in ERA5 forecasts, which are based on a ‘frozen’ model version. In this way the effect of different atmospheric variability on the estimation of skill improvement can be minimized. It is shown to what extent the strength of the polar vortex as measured by the Arctic and North-Atlantic Oscillation (AO, NAO) influences the magnitude of forecast errors. Results may indicate whether recent and future changes in these indices, possibly driven in part by sea-ice decline, could systematically affect the longer-term evolution of medium-range forecast skill.</p>

An Assessment of the Impact of Aeolus Doppler Wind Lidar Observations for Use in Numerical Weather Prediction at ECMWF

2020 ◽  
Author(s):  
Michael P. Rennie ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Forecast Errors ◽  
Horizontal Line ◽  
Numerical Weather ◽  
Medium Range ◽  
Ecmwf Model ◽  
The Impact

<p>The European Space Agency’s Aeolus mission, which was launched in August 2018, provides profiles of horizontal line-of-sight (HLOS) wind observations from a polar orbiting satellite.  The European Centre For Medium-Range Weather Forecasts (ECMWF) began the operational assimilation of Aeolus Level-2B winds on 9 January 2020 in their global NWP (Numerical Weather Prediction) model, 1 year and 4 months after the first Level-2B wind products were produced in near real time via ESA’s ground processing segment.  This achievement was possible because of the production of good data quality, which was met through a close collaboration of all the parties involved within the Aeolus Data Innovation and Science Cluster (DISC) and via the great efforts of ESA, industry and ground processing algorithms pre- and post-launch.<br>Through the careful assessment of the statistics of differences of the Aeolus winds relative to the ECMWF model, the Level-2B Rayleigh winds were found to have large systematic errors.  The systematic errors were found to be highly correlated with ALADIN’s (Atmospheric Laser Doppler Instrument) primary mirror temperatures, which vary in a complex manner due to the variation in Earthshine and thermal control of the mirror.  The correction of this source of bias in the ground processing is underway, therefore in the meantime a bias correction scheme using the ECMWF model as a reference was developed for successful data assimilation; the scheme will be described.  <br>We will present the results of the Aeolus NWP impact assessment which led to the decision to go operational.  Aeolus’ second laser (FM-B, available since late June 2019) provides statistically significant positive impact of moderate to large amplitude, of similar magnitude to some other important and well-established observing systems (such as IR radiances, GNNS radio occultation and Atmospheric Motion Vectors).  Observing System Experiments demonstrate reduction of forecast errors in geopotential and vector wind of around 2% in the tropics and 2-3% in the southern hemisphere for short-range and medium range forecasts (up to day 10).  This positive impact is particularly impressive given that Aeolus provides less than 1% of the total number of observations assimilated, showing the value of direct wind observations for global NWP.</p>

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

scholarly journals Diagnosis of Track Forecast Errors for Tropical Cyclone Rita (2005) Using GEFS Reforecasts

2015 ◽  
Vol 30 (5) ◽  
pp. 1334-1354 ◽  
Author(s):  
Thomas J. Galarneau ◽  
Thomas M. Hamill
Keyword(s):  
Tropical Cyclone ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Track Forecast ◽  
Forecast Errors ◽  
Subtropical Anticyclone ◽  
Cutoff Low ◽  
Layer Flow ◽  
Upper Level ◽  
Mass Evacuation

Abstract Analysis and diagnosis of the track forecasts for Tropical Cyclone (TC) Rita (2005) from the Global Ensemble Forecast System (GEFS) reforecast dataset is presented. The operational numerical weather prediction guidance and GEFS reforecasts initialized at 0000 UTC 20–22 September 2005, 2–4 days prior to landfall, were all characterized by a persistent left-of-track error. The numerical guidance indicated a significant threat of landfall for the Houston, Texas, region on 24 September. The largest mass evacuation in U.S. history was ordered, with the evacuation resulting in more fatalities than TC Rita itself. TC Rita made landfall along the Texas–Louisiana coastal zone on 24 September. This study utilizes forecasts from the GEFS reforecast and a high-resolution regional reforecast. The regional reforecast was generated using the Advanced Hurricane Weather Research and Forecasting Model (AHW) with the GEFS reforecasts providing the initial and boundary conditions. The results show that TC Rita’s track was sensitive to errors in both the synoptic-scale flow and TC intensity. Within the GEFS reforecast ensemble, the nonrecurving members were characterized by a midlevel subtropical anticyclone that extended too far south and west over the southern United States, and an upper-level cutoff low west and anticyclone east of TC Rita that were too weak. The AHW regional reforecast ensemble further highlighted the role of intensity and steering-layer depth in TC Rita’s track. While the AHW forecast was initialized with a TC that was too weak, the ensemble members that were able to intensify TC Rita more rapidly produced a better track forecast because the TCs followed a deeper steering-layer flow.

scholarly journals Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

2016 ◽  
Author(s):  
Emlyn M. Jones ◽  
Mark E. Baird ◽  
Mathieu Mongin ◽  
John Parslow ◽  
Jenny Skerratt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Great Barrier Reef ◽  
Forecast Error ◽  
Weather Prediction ◽  
Biogeochemical Model ◽  
Forecast Errors ◽  
Barrier Reef ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Assimilation System

Abstract. Skilful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically-derived relationships between IOPs and variables such as Chlorophyll-a concentration (Chl-a), Total Suspended Solids (TSS) and Color Dissolved Organic Matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due the additional signal from bottom reflectance. Rather than assimilate quantities calculated using error-prone IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance. The assimilation of a directly-observed quantity, in this case remote-sensing reflectance, is analogous to the assimilation of temperature brightness in Numerical Weather Prediction (NWP), or along-track sea-surface height in hydrodynamic models. To assimilate the observed reflectance, we use an in-water optical model to produce an equivalent simulated remote-sensing reflectance, and calculate the mis-match between the observed and simulated quantities to constrain the BGC model with a Deterministic Ensemble Kalman Filter (DEnKF). Using the assumption that simulated surface Chl-a is equivalent to remotely-sensed OC3M estimate of Chl-a resulted in a forecast error of approximately 75 %. Alternatively, assimilation of remote-sensing reflectance resulted in a forecast error of less than 40 %. Thus, in the coastal waters of the GBR, assimilating remote-sensing reflectance halved the forecast errors. When the analysis and forecast fields from the assimilation system are compared with the non-assimilating model, an independent comparison to in-situ observations of Chl-a, TSS, and dissolved inorganic nutrients (NO3, NH4 and DIP) show that errors are reduced by up to 90 %. In all cases, the assimilation system improves the result compared to the non-assimilating model. This approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially-derived TSS and CDOM, or the lack of a calibrated regional IOP algorithm.

scholarly journals Understanding the relationship between clouds and surface downward radiation forecast errors with Unsupervised Deep Learning

2021 ◽  
Author(s):  
Matthias Zech ◽  
Lueder von Bremen
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Prediction Models ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Cloud Formation ◽  
Trade Wind ◽  
Forecast Errors ◽  
Model Learning ◽  
Unsupervised Deep Learning

<p>Cloudiness is a difficult parameter to forecast and has improved relatively little over the last decade in numerical weather prediction models as the EMCWF IFS. However, surface downward solar radiation forecast (ssrd) errors are becoming more important with higher penetration of photovoltaics in Europe as forecasts errors induce power imbalances that might lead to high balancing costs. This study continues recent approaches to better understand clouds using satellite images with Deep Learning. Unlike other studies which focus on shallow trade wind cumulus clouds over the ocean, this study investigates the European land area. To better understand the clouds, we use the daily MODIS optical cloud thickness product which shows both water and ice phase of the cloud. This allows to consider both cloud structure and cloud formation during learning. It is also much easier to distinguish between snow and cloud in contrast to using visible bands. Methodologically, it uses the Unsupervised Learning approach <em>tile2vec</em> to derive a lower dimensional representation of the clouds. Three cloud regions with two similar neighboring tiles and one tile from a different time and location are sampled to learn lower-rank embeddings. In contrast to the initial <em>tile2vec</em> implementation, this study does not sample arbitrarily distant tiles but uses the fractal dimension of the clouds in a pseudo-random sampling fashion to improve model learning.</p><p>The usefulness of the cloud segments is shown by applying them in a case study to investigate statistical properties of ssrd forecast errors over Europe which are derived from hourly ECMWF IFS forecasts and ERA5 reanalysis data. This study shows how Unsupervised Learning has high potential despite its relatively low usage compared to Supervised Learning in academia. It further shows, how the generated land cloud product can be used to better characterize ssrd forecast errors over Europe.</p>

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