The role of scatterometry in ocean model forcing

VI Expanding Ocean Frontiers Conference, 5-7 July 2021, Barcelona, Spain.-- 25 page

Extrem, o no tan extrem, aquesta és la qüestió

3 pages, 2 figures[EN] This proves to be a question that is difficult to answer, but has far-reaching consequences for satellite meteorology, weather forecasting, oceanography, climate and hurricane advisories. Hurricanes are among the deadliest of the existing natural disasters, moreover causing formidable economic losses (Bevere et al. 2020). Accurate, short- and medium-range forecasting of their intensity and track (among others) are therefore essential to mitigate human and economic losses. In the longer range, it is also important to understand whether extreme weather conditions are becoming more extreme in a changing climate, stirring deeper waters in the ocean and hence affecting climate system dynamics. Unfortunately, tropical circulation conditions, such as El Niño and the Madden Julian Oscillation, are associated with large year-to-year variability in extreme wind speed distribution and their link to climate change is poorly understood, limiting our ability to determine whether the hurricane climatology is actually changing or not. […][ES] Esta es una pregunta difícil de responder, pero que tiene consecuencias de gran alcance para la meteorología satelital, la previsión meteorológica, la oceanografía, el clima y los programas de aviso de huracanes. Los huracanes se encuentran entre los desastres naturales más mortíferos y, además, causan enormes pérdidas económicas (Bevere et al. 2020). Por lo tanto, una predicción precisa de su intensidad y trayectoria a corto y medio plazo son esenciales para mitigar las pérdidas humanas y económicas. A más largo plazo, también es importante comprender si las condiciones meteorológicas extremas se están volviendo más extremas en el contexto del cambio climático, llegando a perturbar aguas más profundas y, por lo tanto, afectando la dinámica del sistema climático entero. Desafortunadamente, fenómenos como El Niño y la Oscilación de Madden-Julian, están asociados a una gran variabilidad interanual en la distribución de la intensidad de vientos extremos, con una dependencia del cambio climático todavía poco clara, limitando así nuestra capacidad para determinar si la climatología de huracanes en realidad está cambiando o no. […][CAT] Aquesta és una pregunta difícil de respondre, però que té conseqüències de gran abast per a la meteorologia satel·litària, la previsió meteorològica, l’oceanografia, el clima i els programes d’avís d’huracans. Els huracans es troben entre els desastres naturals més mortífers i, a més, causen enormes pèrdues econòmiques (Bevere et al. 2020). Per tant, una predicció precisa de la seva intensitat i trajectòria a curt i mitjà termini són essencials per a mitigar les pèrdues humanes i econòmiques. A més llarg termini, també és important comprendre si les condicions meteorològiques extremes s’estan tornant més extremes en el context del canvi climàtic, arribant a pertorbar aigües més profundes i, per tant, afectant la dinàmica del sistema climàtic sencer. Desafortunadament, fenòmens com El Niño i l’Oscil·lació de Madden-Julian estan associats a una gran variabilitat interanual en la distribució de la intensitat de vents extrems, amb una dependència del canvi climàtic encara poc clara, limitant així la nostra capacitat per a determinar si la climatologia d’huracans en realitat està canviant o no. […]Peer reviewe

High-Quality Graphene Using Boudouard Reaction

Following the game-changing high-pressure CO (HiPco) process that established the first facile route toward large-scale production of single-walled carbon nanotubes, CO synthesis of cm-sized graphene crystals of ultra-high purity grown during tens of minutes is proposed. The Boudouard reaction serves for the first time to produce individual monolayer structures on the surface of a metal catalyst, thereby providing a chemical vapor deposition technique free from molecular and atomic hydrogen as well as vacuum conditions. This approach facilitates inhibition of the graphene nucleation from the CO/CO2 mixture and maintains a high growth rate of graphene seeds reaching large-scale monocrystals. Unique features of the Boudouard reaction coupled with CO-driven catalyst engineering ensure not only suppression of the second layer growth but also provide a simple and reliable technique for surface cleaning. Aside from being a novel carbon source, carbon monoxide ensures peculiar modification of catalyst and in general opens avenues for breakthrough graphene-catalyst composite production

Proposal and Evaluation of the Machine Learning Models for Correcting ERA5 Stress Equivalent Wind Forecasts as a Function of Atmospheric and Oceanic Conditions

Trabajo final presentado por Evgeniia Makarova para un máster en Data Science de la Universidad Autónoma de Madrid (UAM), realizado bajo la dirección del Dr. Marcos Portabella Arnús del Institut de Ciències del Mar (ICM-CSIC).-- 71 pagesThis work aims at creating a preliminary machine learning (ML) model for correcting the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis stress-equivalent local wind biases, based on atmospheric and oceanic parameters. Several errors in the ECMWF global output for near surface ocean winds have been reported when validated against scatterometer observations. An existing approach for correcting these biases (the so-called ERA* method) consists of scatterometer-based corrections accumulated over a certain time window at each grid point, which allows to reduce local persistent biases. This approach is sensitive to scatterometer sampling and, to collect a statistically significant number of samples, assumes that such biases are static. This is not the case for errors due to moist convection or the diurnal cycle. For operational purposes, the temporal window is lagged with respect to the reanalysis forecast time and the time difference between scatterometer-based correction (SC) and sample data collections can be ten days. We propose a preliminary ML setup that looks for the functional relationship between several oceanic and atmospheric variables that describe the persistent NWP errors as observed in the NWP-scatterometer differences. This would allow to predict the biases of the stressequivalent wind forecasts and using the bias corrections in coupled weather or seasonal forecasts, or to account for these in climate runs. Such variables are first identified as ECMWF model parameters, such as stress-equivalent winds, their derivatives (curl and divergence), atmospheric stability related parameters, i.e., sea-surface temperature (SST), air temperature (Ta), relative humidity (rh), surface pressure (sp), as well as SST gradients and ocean currents. This work evaluates the feasibility of such approach and provides an overview of possible implementations of this regression. Several ML algorithms are trained on a dataset that covers a period of 65 days and further evaluated. These algorithms include two libraries based on Gradient Boosting Decision Trees (GBDT), such as XGBoost and LightGBM, and feed-forward neural networks, implemented with the sklearn library (MLP Regressor) and with the Tensorflow and Keras API. The models are trained to reproduce the differences between collocated scatterometer (ASCAT-A) and ERA5 U10S. The resulting models are further evaluated against a test dataset that covers a period of 23 days posterior to the training period. The best performing models are then further selected to generate the corrections for the entire ERA5 forecasts. The corrected forecasts are then collocated with an independent scatterometer HSCAT-B that has a local pass time that differs 3.5 hours from that of ASCAT-A. Globally, the best performing model is a Tensorflow-based neural network with 4 hidden layers with 256, 128, 64, 32 neurons per layer, with dropout used for regularization. It shows a 5.54% of square error reduction globally, and in particular up to 7.66% in the extra-tropics, compared to ERA5 (test period). In the tropics and high latitudes, the error variance reduction is of 3.67% and 5.47%, respectively. This neural network setup outperforms the ERA* product in the extra-tropics and high latitudes, although not in the tropics. This work demonstrates that it is possible to reduce ERA5 local biases by using only NWP variables as model inputs, which makes this approach promising for operational setup purposesPeer reviewe

On the use of machine learning to correct NWP model sea surface wind forecasts with scatterometer data input

ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics, 2-10 November 2023This work aims at creating a preliminary machine learning (ML) model for correcting the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis stress equivalent local wind biases. Several preliminary ML setups are evaluated, which look for the functional relationship between several oceanic and atmospheric variables and the persistent NWP biases as observed in the NWP-scatterometer differences. Such variables include the ECMWF model parameters, such as stress-equivalent winds and their derivatives (curl and divergence), atmospheric stability-related parameters, i.e., sea-surface temperature (SST), air temperature (Ta), relative humidity (rh), surface pressure (sp), as well as SST gradients and ocean currents. The algorithms that are evaluated include two libraries based on Gradient Boosting Decision Trees (GBDT), such as XGBoost and LightGBM, and feed-forward neural networks (FNNs), implemented with the sklearn (MLP Regressor) and with Tensorflow. Globally, the best performing model is a Tensorflow-based neural network with 4 hidden layers with 256, 128, 64, 32 neurons per layer. It shows a 5.54% of error variance reduction globally, and in particular up to 7.66% in the extra-tropics, when compared to the ERA5 performance (test period). In the tropics and high latitudes, the error variance reduction is of 3.67% and 5.47%, respectively. The work demonstrates the feasibility to predict ERA5 local biases, mainly using information based only on other NWP variables. This can be used in the operational setup for correction of the ECMWF ocean forcing forecasts in line with scatterometer-based bias adjustments applied in data assimilation. Further information can be found at • Eugenia Makarova Proposal and evaluation of the Machine Learning Models for correcting ERA5 stress equivalent wind forecasts as a function of atmospheric and oceanic conditions. Master thesis; Advisor: Marcos Portabella (December 2022). Follow the LinkPeer reviewe

Evaluation of the ERA* ocean forcing product under storm surge conditions in the Adriatic Sea

Trabajo final presentado por Evgeniia Makarova para el grado de Ciencias Marinas de la Universitat de Barcelona (UB), realizado bajo la dirección del Dr. Marcos Portabella Arnús del Institut de Ciències del Mar (ICM-CSIC) y el profesor Giorgi Khazaradze Tsilosani de la Universitat de Barcelona (UB).-- 32 pages, 16 figures, 6 tablesStorm surges in the Adriatic Sea are periodic extreme weather events that cause high economical losses and endanger human lives. These events are caused by an atmospheric pressure depression and persistent south-easterly winds (Sirocco). This work focuses on further developing and verifying an enhanced ocean forcing product (ERA*) in the Mediterranean Sea, with the aim to improve the storm surge prediction capabilities in the Adriatic Sea region. The ERA* is a corrected ERA-Interim reanalysis product (ERAi) provided by the European Centre for Medium-range Weather Forecasts (ECMWF). A scatterometer-based correction, using high-resolution ocean stress-equivalent winds (U10S) from a scatterometer constellation is proposed to reduce ERAi local U10S biases. Since the local biases are relatively persistent over time, and their persistence is regionally dependent, ERA* has several configurations, which consist of different temporal windows over which the scatterometer-based corrections are applied. The accuracy of the product is being evaluated against an independent 25-km resolution U10S product from the Chinese HY-2A scatterometer HSCAT. [...

Evaluation of the ERA* ocean forcing product under storm surge conditions in the Adriatic Sea

VII International Symposium on Marine Sciences (ISMS 2020), 1-3 July 2020 (Barcelona).-- 2 pagesStorm surges in the Adriatic Sea are periodic extreme weather events that cause high economical losses and endanger human lives. These events are caused by an atmospheric pressure depression and persistent south-easterly winds (Sirocco). This work focuses on further developing and verifying an enhanced ocean forcing product (ERA*) in the Mediterranean Sea, with the aim to improve the storm surge prediction capabilities in the Adriatic Sea region [1], [2]. The ERA* is a corrected ERA-Interim reanalysis product (ERAi) provided by the European Centre for Medium-range Weather Forecasts (ECMWF)[3]. A scatterometer-based correction, using high spatial resolution ocean stress-equivalent winds (U10S) from complementary scatterometers (ASCAT-A/B, and OSCAT), is proposed to reduce ERAi local U10S biases. Since local biases are relatively persistent over time but their persistence is regionally dependent, ERA* has several configurations of the temporal windows applied to calculate the scatterometer-based corrections. The accuracy of the product is being evaluated against a 25-km resolution U10S product froman independent scatterometer (HSCAT). On the global scale, Trindade et al. [3] show that ERA*, in 2 or 3-day temporal window configuration, outperforms the ERAi product, showing about a 10% error variance reduction when compared to HSCAT. In the present work, the tests for the Mediterranean region, which generally has higher wind variability (and therefore reduced persistence), show that the ERA* 3-day configuration product still outperforms ERAi, but presents higher errors than in open ocean tropical and extra-tropical regions. For the Adriatic Sea, both ERAi and ERA* products show similar quality, although ERA* shows smaller zonal and meridional U10S biases than ERAi. Similar results are obtained when evaluating the product under storm surge conditions in the Adriatic. Due to rapidly changing winds in the Mediterranean, and more in particular in the Adriatic region, where coastal effects are more prominent, we also plan to verify the quality of the ERA* 1-day configuration, notably during storm surge eventsPeer reviewe

World Ocean Circulation Product User Manual for ERAstar v1.0

The present document is the Product User Manual dedicated to the content and format description of the ERA star stress-equivalent wind vector (U10S) and wind stress product. This is the primary document that users should read before handling the product. It provides an overview of processing algorithm, technical product content and format and main validation resultsPeer reviewe

World Ocean Circulation Algorithm Technical Baseline Document for ERAstar v1.0

The ERA* stress-equivalent wind (U10S) and stress vector product version 1.0 is a correction of the ECMWF Fifth Reanalysis (ERA5) output by means of geo-located scatterometer-ERA5 differences over a 3-day temporal window, in which the combined sampling of the Advanced Scatterometers on board the Metop satellite series (ASCAT-A, -B, and -C) and the SCATSat-1 scatterometer (OSCAT2) have been used, for the year 2019. ERA* can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as WBCS, highly stationary), wind effects associated with the ocean mesoscales (SST), coastal effects (land see breezes, katabatic winds), PBL parameterization errors, and large-scale circulation effects, e.g., at the ITCZPeer reviewe

WOC ERA* Hourly Global Stress Equivalent Wind and Wind Stress (V.2.0) [Dataset]

Project World Ocean Circulation (WOC).-- Data acquisition: Scatterometer datasets (ASCAT-A,B,C, OSCAT and OSCAT2) and stress-equivalent ERA5 winds provided by Royal Netherlands Meteorological Institute (KNMI). Filenames: 2020010103-WOC-L4-STRESS_ERAstar_GLO_0125_1H_R20191231T18_09-v2.0-fv1.0.nc. Sensor: ASCAT-A, -B, -C onboard the EUMETSAT Metop satellite series, OSCAT onboard the Oceansat-2 and OSCAT2 onboard SCATSat-1. Spatial resolution: 0.125 degree. Spatial grid: WGS 84 / Regular longitude-latitudeThe ERA* stress-equivalent wind (U10S) and stress vector product is a correction of the ECMWF ERA5 output by means of geo-located scatterometer-ERA5 differences over a few days temporal window. ERA* can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as WBCS, highly stationary), wind effects associated with the ocean mesoscales (SST), coastal effects (land see breezes, katabatic winds), PBL parameterization errors, and large-scale circulation effects, e.g., at the ITCZESA Contract No. 4000130730/20/I-NBL4 Erastar stress equivalent model wind u component at 10 m, erastar stress equivalent model wind v component at 10 m, erastar eastward wind stress, erastar northward wind stress, era5 stress equivalent model wind u component at 10 m, era5 stress equivalent model wind v component at 10 m, era5 eastward wind stress, era5 northward wind stress, number of scatterometer samples, land sea ice quality flagPeer reviewe