Evaluation of the Era* Ocean Forcing Product Under Storm Surge Conditions in the Adriatic Sea ______

BARCHELOR’S THESIS (TFG) ______EVALUATION OF THE ERA* OCEAN FORCING PRODUCT UNDER STORM SURGE CONDITIONS IN THE ADRIATIC SEA ______

Evgeniia Makarova

Degree in Marine Sciences University of Barcelona

External tutor: Marcos Portabella Arnús Internal tutor: Giorgi Khazaradze Tsilosani External entity: Institute of Marine Sciences (CSIC)

June 2020

Abstract

Storm 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.

While ERA* performance on the global scale is optimized with a 2 to 3 day temporal window configuration, in the Mediterranean, and especially the Adriatic Sea, ERA* 1-day configuration shows the best performance. This is due to increased wind variability conditions in the Mediterranean, and in particular in the Adriatic region, where the coastal effects are most prominent, thus reducing the ERAi local bias persistence. For the Mediterranean Sea, the ERA* 1-day configuration has an error variance (w.r.t. HSCAT) 16% smaller to that of ERAi. Similar results are obtained in the Adriatic Sea. For the storm surge periods, which include 5 days prior and 2 days after the Sirocco event, showing high wind variability conditions, ERA* 1-day outperforms ERAi with a reduced error variance of about 9%. Moreover, when assessing the performance over the strong Sirocco wind period only, ERA* 1-day shows an error variance reduction of about 37% with respect to that of ERAi. Moreover, ERA* 1-day substantially reduces the systematic ERAi underestimation of the strong winds that precede extreme surge events.

Lay Summary

One of the most important hazards that regularly affects the city of Venice (Italy) and its surrounding areas are the so-called Acqua Alta events. During one the most recent events that happened in November 2019 the medium sea level rose up to 187 cm, flooding 80% of the city (Fig. 1) (Masters, 2019).

Figure 1. San Marco square during Aqua Alta event in November 2019. (Venezia, nuovo picco d’acqua alta, 2019) These floods happen when a combination of certain meteorological conditions occur at the same time, such as low pressure and the presence of persistent strong south- easterly winds, called Sirocco. This causes a sea level rise in the northern part of the Adriatic Sea basin. Such tidal sea level rises are generally being called storm surges, which can be amplified by a physical phenomenon called Seiches that can occur in closed and semi-closed basins. Seiches are standing waves caused by the water level oscillation in the basin (NOAA, 2018). The most important inputs, that are being used in the prediction of the sea level rise in the area, are the numerical weather prediction (NWP) forecasts on the atmosphere pressure over Mediterranean and winds over the Adriatic Sea. The accuracy of the predicted sea surface wind speed and direction has a big influence on the sea level rise predictions. The precise prediction of the sea level is required for the proper functioning of the alarm and warning systems, and for the future protection system MOSE (a barrier that can protect the city of Venice from a sea level rise of up to 3 meters).

The ocean models use as ocean forcing input the NWP sea surface wind output, such as that of the European Centre for Medium-range Weather Forecasts (ECMWF). The latter often underestimates strong Sirocco wind events (Zecchetto et al., 2015), which in turn leads to an underestimation of the predicted sea level rise. The ERA* product was designed to improve the accuracy of the ocean forcing, as derived from ECMWF model output, by using the datasets of the winds measured by satellite radar sensors, called scatterometers. These sensors are capable of accurately estimating the sea surface wind vector over the water basins by measuring the surface roughness that is a function of the winds blowing over the surface. ERA* calculates the mean difference between the sea surface winds measured by space-borne scatterometers and those from ECMWF forecasts over a temporal window of typically a few days, and applies this correction to the forecast, at each lead time. The different ERA* configurations are based on different temporal windows over which the scatterometer-model differences are computed, typically ranging from 1 to 5 days. The aim of ERA* is to correct for NWP local biases, which can reach up to 1-2 m/s. ERA* was tested on a global scale, and separately over the tropics and middle latitudes, showing its best performance over a 3-day temporal window configuration, outperforming ECMWF ERA-Interim product (ERAi) sea surface wind performance, with a reduced error variance of about 10%. The ERA* performance in the tropics, which are dominated by the very persistent trade winds, is better than in the extra-tropics where fast evolving systems occur, thus reducing the persistence of the reported NWP local biases. In the Mediterranean Sea, on the other hand, and especially in the Adriatic Sea, the winds are more variable and can change in a matter of hours. In this work we compare the performance of the ERA* 1-day and 3-day configurations against the original ERAi in the Mediterranean and Adriatic Seas and studied the product behaviour in case of storm surge events. Due to the high wind variability conditions in the area and relatively good coverage of the scatterometer constellation, we verify that the ERA* 1-day configuration has best accuracy, using independent scatterometer data as reference. In case of the Sirocco wind events causing Acqua Alta, the ERA* 1-day error reduction can be substantial, notably for winds over 10 m/s, thus mitigating the sea surface wind speed underestimation of the original ERAi forecasts. Using the ERA* 1-day product as ocean forcing input to the sea level prediction models could improve the accuracy of the flood predictions, their magnitude and possible impact.

How COVID-19 lockdown impacted this work

Fortunately, this work consists of analysing already available datasets, which doesn’t require any presential work in the laboratory. The requirements to carry on with this project were the availability of the Matlab licence (that almost expired during the project and got renewed just before the expiration date), a computer that could cope with the processing of the large amounts of data and a good internet connection with a high bandwidth. Fortunately, I was able to perform most of the heavy calculations locally, since a remote access to the ICM server infrastructure is only permitted to the ICM employees. At some point in time, I had to download about 200 gigabytes of data from the ICM shared folder to be able to progress with the work, so not having a really good internet connection and sufficient storage could have been an issue. The most important impact was due to the lack of the face to face communication that normally helps to resolve the issues in a faster manner compared to the email communication, as well as stimulates the generation of new ideas during brainstorming sessions. Another important problem was the working conditions at home as I had constant noise (around 70 dB) from 8 am to 6 pm from the construction site in front of my building. The strict lockdown during 6 weeks with no ability to go out for a walk and to see anything else than the buildings of my block, while going to the supermarket, definitely had an impact on my state of mind, reducing the overall productivity, as well as on the mood of my flatmates, who caused some additional stress. The good thing is that the deadline for the presentation of this work was extended by a month, which gave me more time to complete the pending course-work, with the corresponding works to be handed in, and dedicate more time to this report. In general, I think that compared to my colleagues that were due to present their final degree project this summer, I suffered a relatively small impact on my project due to the lockdown.

Table of Contents

1. Introduction ...... 1 1.1 Numerical Weather Prediction Models and ERA-Interim reanalysis product ...... 1 1.2 Scatterometers and sea-surface wind observations ...... 3 1.3 Storm Surge predictions in the Adriatic Sea ...... 4 1.4 Storm surge forecasting, warning and protection systems ...... 5 1.5 ECMWF NWP wind forecasts accuracy in the Adriatic basin ...... 6 2. Objectives ...... 7 3. Data Sets ...... 8 3.1 ERA-Interim ...... 8 3.2 Scatterometers ...... 9 3.3 ERA* ...... 10 4. Methodology ...... 11 5. Results ...... 15 5.1 3-month accumulated metrics over the Mediterranean and Adriatic Seas ...... 15 5.2 Metrics for the storm surge events in the Adriatic Sea ...... 16 6. Discussion ...... 20 7. Conclusions ...... 26 8. Bibliography ...... 28 9. Acknowledgements ...... 32

1. Introduction

1.1 Numerical Weather Prediction Models and ERA-Interim reanalysis product

A reliable and accurate estimation of the sea surface winds is crucial for successful surface wave and surge model predictions. The Numerical weather predictions (NWP) model output is commonly used to force ocean models, since there is a lack of high spatial and temporal resolution of sea surface wind observations. One of the most commonly used NWP models is the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-Interim (hereafter referred to as ERAi) (Trindade et al., 2020).

The model has several issues and one of them is its relatively poor resolution of open-ocean small-scale dynamics, which together with parameterization errors, or poor sea surface temperature (SST) and sea surface wind coupling, among others, lead to relevant errors. The ocean surface winds derived from the scatterometers have a greater accuracy and spatial resolution than those of ERAi. Moreover, scatterometers provide a close measure of the ocean forcing, i.e., stress-equivalent winds or U10S (De Kloe et al., 2017), which are relative to the ocean motion, while ERAi provides winds relative to a fixed Earth. The resolution of the scatterometer winds is about 25 km (Trindade et al., 2020), while that of the global NWP winds is about 100-150 km in open ocean (Vogelzang et al., 2011).

In contrast, scatterometers have a relatively poor temporal sampling, i.e., a scatterometer may sample the winds over a certain location only up to twice a day (Trindade et al., 2020), while NWP models are ubiquitous.

The above-mentioned NWP errors lead to local and persistent biases of up to 1-2 m/s, as reported by Belmonte and Stoffelen (2019). Such persistent biases in the ocean- atmospheric interaction can be resolved by combining scatterometer data with the NWP estimates, thus increasing the temporal resolution as compared to direct scatterometer observations, and the spatial resolution as compared to that of the global NWP output. The analysis of the differences between the ERAi model and real scatterometer measurements over a certain period of time can be used to correct local, persistent ocean forcing biases in the ERAi field due to physical processes absent or misestimated by the model, e.g., current effects, coastal effects (land-sea breezes, gap winds), or large-scales circulation effects.

However, the resulting product generated by blending different spatial scales and different physical processes with the observed biases might present rather artificial spatio- temporal characteristics (Trindade et al., 2020), with noticeable transition areas between

1 the sites where the satellite measures were obtained and the corrections applied and where only NWP data was available.

The ERA* product, evaluated in this work, consists of a scatterometer-based correction to the existing ERAi product (described later in the data sets section). The correction consists of geo-located temporally averaged wind component differences between scatterometer wind data and collocated ERAi winds. The 10-m neutral ERAi winds (i.e., the sea surface winds that would have been observed with a neutrally stratified surface layer, e.g., (Bourassa, 1998; M. Portabella & Stoffelen, 2009) are being converted to 10-m stress-equivalent winds (U10S), that are more compatible with the scatterometer retrievals (Kloe et al., 2017). The correction that is applied can be calculated using wind datasets from several scatterometers at a time, and averaged over the different temporal windows, ranging from 1 to 5 days (see example in Fig. 1).

Figure 1. Scatterometer correction (SC) for 15th January 2013 between ASCAT-A and ERAi U10S for (a) zonal and (b) meridional wind components, accumulated over a 5-day temporal window (adopted from Fig 1. Trindade et al., 2020).

Based on the previous study for the tropics and the middle latitudes (Trindade et al., 2020), on the global scale, ERA* product shows higher resolution and accuracy than the original ERAi product. The best results are obtained using the configuration with 3 available scatterometers (i.e., ASCAT onboard Metop-A & B, and OSCAT onboard Oceansat-2), and with a temporal window of 2 or 3 days. The product shows good potential, with a global error variance reduction of about 10% with respect to that of ERAi, notably in the areas with persistent local conditions such as the trade winds region.

In this work we will focus on the evaluation of the ERA* product performance in the semi-closed water basin of the Mediterranean, and in particular, that of the Adriatic, which have much higher wind variability compared to that of the open ocean due to the proximity of the coast and the influence of the local topographic features.

1.2 Scatterometers and sea-surface wind observations

In the past, most of the weather data was collected over land and the knowledge of the surface winds over the oceans mostly came from irregular reports from the ships and buoys. Now satellites are being used to provide meteorological information over the global ocean, over which we have sparse in-situ measurements. There are two types of sea surface wind sensitive remote sensing instruments: passive and active microwave sensors. The passive sensors measure the electromagnetic radiation coming from the Earth surface and the atmosphere and several meteorological parameters can be obtained from such measurements, including winds (Marcos Portabella, 2002). Passive microwave sensors (radiometers) measure the wind speed over the ocean by analysing the power spectrum of the electromagnetic radiation that is emitted by the wind roughened surface (Bourassa et al., 2019). The surface roughness depends on the near surface wind speed. The accuracy of the retrievals of the radiometers decreases in the presence of rain (Marcos Portabella, 2002). The active microwave sensors (radars) emit electro-magnetic radiation towards the Earth and measure the properties of the received reflected signal. The scatterometers measure the power backscattered from the surface roughness (Fig. 2). At the radar operating wavelength and incidence angle, is the backscatter radiation mostly comes from the capillary waves that are in the equilibrium with the local wind stress. The measured backscatter depends on the magnitude of the stress and the stress direction relative to the radar beam (Liu, 2015). Scatterometer instruments provide accurate all-weather sea surface winds, although the quality is affected by the presence of rain, with the longer wavelength C-band instruments (i.e. ASCAT), performing better than the Ku-band instruments (i.e. OSCAT) (Bourassa et al., 2019). More technical information on the scatterometers used in this work is provided further in the data sets section.

Figure 2. Schematic representation of the scatterometer operation over the ocean surface (adopted from Nie, 2008).

1.3 Storm Surge predictions in the Adriatic Sea

The northern Adriatic Sea is affected by storm surge events, which often cause, e.g., flooding in Venice and the surrounding areas. Storm surges consists of intense rising of the sea level, caused by severe meteorological conditions. They can be extremely destructive in proximity of low-lying coastal areas and can result in extensive flooding which can lead to the loss of life and significant economic losses.

The storm surge events that occur in the Adriatic Sea normally take place from autumn to spring and are caused in part by inversed barometric effect but mainly by the winds (Bajo et al., 2017). One of the common winds in the area is called Sirocco, i.e., persistent south-easterly winds that can last for days (Biasio et al., 2017). This steady, warm and moist wind is tunnelled by the coastal orography of the Adriatic Sea and can reach up to 20 m/s (Bajo et al., 2017). This wind is pushing the water towards the northern shallow closed end of the Adriatic Sea causing the sea level rise.

Besides the piling up of the water mass by the persistent wind, this phenomenon is amplified by seiches caused by the basin’s morphology. Seiches are standing waves oscillating in the basin (NOAA, 2018) and occur as a response to unstable conditions like a horizontal gradient in the water level due to the wind action when the steady Sirocco wind suddenly changes to Bora (north-eastern wind) (Biasio et al., 2017). The period of these standing waves is determined by the bathymetry and the dimensions of the basin, and in the case of the Adriatic Sea, the two major seiches have periods of 21.2h and 10.8h, and produce level displacements of 20-30 cm that can reach up to 60-80 cm (Zecchetto et al., 2015). The periods of the seiches are close to the diurnal and semi-diurnal astronomic tide. This can produce resonance effects due to the combined contribution to the sea level rise of the surge, the astronomic tide and the seiche producing high water levels.

The flooding events (Acqua alta) caused by the combination of storm surge and seiches affect several sites in the Northern Adriatic Sea. However most of the damage is caused in the city of Venice due to the severity of the events and the historical heritage of the city. Venice is located inside a lagoon that is connected to the sea by three inlets. This makes the city protected from the wind waves in case of storms but not against the sea level rises caused by the storm surge events. Part of the city is only 80 cm above the mean sea level and even moderate surges can cause floods during spring tides (Bajo et al., 2017).

The flooding coverage of the town in relation to high water levels (measured by the mareographic zero) are the following: +100 cm corresponds to 3,56% of the town flooded; +110 cm to 11,74%; +120 cm to 35,18%; +130 cm to 68,75%; and over +140 cm to 90% (“High Water Information Centre of the Municipality of Venice”).

The last record sea level rise caused by the storm surge occurred on the 12th of November 2019, causing a sea level rise of 187 cm in Venice and flooding over most of the city area (Masters, 2019). Only in Venice, it caused a total damage estimate of $1.1 billion, impacting a large number of historical monuments that are part of the UNESCO heritage (Cranley, 2019).

With the sea level rise due to the climate change and the subsidence of the city of Venice (around 25 cm in the last century) such extreme flooding events will be occurring more often (Tosi et al., 2013). To be able to reduce the impact from flooding, accurate forecasting is crucial, in particular of the sea surface wind flow in the area.

1.4 Storm surge forecasting, warning and protection systems for the Acqua Alta events

The Centre for Forecast of Tide Level and High-Water Alerting (CPSM) is responsible for monitoring tides, sea-level rise and emitting warnings in case of hazards. CPSM elaborates 48h forecasts three times a day based on the observed data from different monitoring networks and meteorological data, with particular attention to wind data in the Adriatic basin and the atmospheric pressure (CPSM, 2017a).

CPSM uses both statistical and hydrodynamic models. Statistical models are based on the autoregressive scheme with the coefficients calibrated by the correlation of the 25- year data records. This model computes the sea level based on the predictors such as observed sea level, atmospheric pressure forecasts, etc. The statistical NWP provides accurate short-term sea-level forecasting, but its performance is directly dependent on the data records used for calibration as it doesn’t take into count any mathematical description of the physical processes.

The Shallow Water Hydrodynamic Model (SHYFEM) developed at the Institute of Marine Sciences (ISMAR) includes equations that represent the physical system of the

Mediterranean Sea, the Adriatic Sea and the Venice lagoon. This model estimates the processes using sea bathymetry, morphology and wind forcing from the atmospheric fields over the whole geographic area. The model generally shows good performance but, in case of severe storm surges, might strongly underestimate the sea level rise. The forecast precision depends directly on the quality of the atmospheric forcing used by the model. Using higher resolution and more accurate winds should improve the model accuracy, as well as integrating the tide gauge data. (Zampato et al., 2016).

The accuracy of the sea level rise forecasts is crucial not only for the flood warning system but also for the operation of the upcoming protective MOdulo Sperimentale Elettromeccanico, Experimental Electromechanical Module (MOSE) system. MOSE is designed to protect the Venice lagoon from the flooding above a certain level (Fig. 3).

The system consists of an array of barriers deployed in the inlets of the lagoon and the decision of the raising the barrier should be taken 4-5 hours in advance as this is the average closing time of the port inlets (“MOSE System”).

Figure 3: The Mobile Barrier System (MOSE) in the Venice Lagoon (Gudmestad, 2015).

1.5 ECMWF NWP wind forecasts accuracy in the Adriatic basin and possible improvement

As mentioned in the previous section, the quality of the storm surge forecasts is strictly related to the accuracy of the meteorological input used by the storm surge model, i.e., the NWP output, including the sea surface wind and the atmospheric pressure fields. In the Adriatic Sea, the ECMWF sea surface wind output is generally underestimated compared to the winds obtained from the scatterometers (Zecchetto et al., 2015). These biases are more important in the coastal areas due to poor representation of the physical processes occurring at the land-sea interface and the inability of the ERAi model to capture small scale wind variability due to its limited resolution.

Using scatterometer winds to adjust the ERAi model might enhance the forecast accuracy. However, the effectiveness of a scatterometer-based correction is highly

6 dependent on the scatterometer sampling (i.e., the number of scatterometers) in the area. Also note that in presence of the heavy precipitation, the Ku-band scatterometer systems undergo a more strict quality control (Marcos Portabella, 2002), which can potentially impact the scatterometer sampling.

The importance of the accuracy of the wind forecasts for predicting the Aqua Alta events is described in one the of the examples of the flooding that happened on the December 1st 2008 by Bajo et al., 2017. The maximum wind speed recorded by AAPTF (Acqua Alta Platform) was about 20 m/s and the maximum sea level rise was 151 cm, which produced the inundation of 75% of the city of Venice. The flood was underestimated by the Centre of the forecast of the tide level (CPSM) while using statistical forecast models. The wind forecasted by ECMWF was underestimated by 17% which with the ongoing seiche reduced the forecast accuracy.

In case of the event on November 12th 2019 the CPSM official forecast, based on statistical and deterministic models developed by ISMAR, underestimated the flooding (145 cm instead of 187 cm) mainly due to underestimation and errors in the NWP wind fields over the Adriatic Sea and the Venice lagoon (“Record Venice Acqua Alta” 2019).

2. Objectives

The objective of this work is to evaluate the ERA* product performance compared to that of the ECMWF ERAi during the storm surge events occurring in the Adriatic Sea. For a better understanding of how this product behaves in semi-closed basins we begin by assessing the product over a larger area, i.e., the whole Mediterranean Sea and a larger period of time (w.r.t. that of the storm surge events), i.e.,3 months.

In this work we compare the performance of two ERA* configurations, one using 3- day mean scatterometer-based corrections and the other using 1-day corrections. The reason for including a shorter temporal window (1 day) than that used for the global ocean (3 days) in the analysis is to assess ERA* in the context of increased wind variability conditions as those found in the Mediterranean region. The 1-day correction may give a better precision in case of rapidly changing local winds, but its quality is highly dependent on the scatterometer constellation coverage of the region of interest.

Note that those sea areas not covered by scatterometer swaths over the specified temporal window don’t have any scatterometer correction applied and therefore will contain the original ERAi wind information. These gaps between swaths lead to no accuracy improvement and to certain artefacts on the swaths’ edges. We therefore also analyse if these gaps significantly reduce the performance of the product in its 1-day configuration in the area of study.

3. Data Sets

The ERA* product used in this analysis is based on the 2013 U10S product data sets derived from different scatterometer systems: the Advanced Scatterometers (ASCAT-A and ASCAT-B) onboard Metop-A and B platforms, and the Oceansat-2 scatterometer (OSCAT) (Trindade et al., 2020). The ERAi data set is retrieved from the ECMWF’s Meteorological Archival and Retrieval System (MARS) for the same period. The ERA* product is verified against independent scatterometer data from the 25 km HSCAT U10S product.

The data sets analysed cover the period between February 1st and April 30th, 2013. During this period, two specific storm surge events occur, which produced a sea level rise over 110 cm at the Punta della Salute (CPSM, 2017):

• On February 12th, with a reported maximum rise of 143cm at 00:05. • On March 31th, with a maximum rise of 125 cm at 00:10.

For both events we use 8-day data sets with 5 days prior to the event and 3 days after. The resulting periods analysed are 7-14 February and 26 March - 2 April 2013.

A short introduction to each of the datasets used in the statistical analysis as well as used in the ERA* product is provided as follows.

3.1 ERA-Interim

ERA-Interim is a climate reanalysis dataset that uses IFS – CY31r2 version of ECMWF NWP that assimilates observations datasets from various sources. The horizontal grid spacing of the ERAi atmospheric model is about 80 km. The forecasts are run twice a day at 00 and 12 UTC and the output (forecasts) for surface parameters are generated every 3 hours for a period of 10 days (Berrisford et al., 2011). The analysis of the atmospheric fields are available every 6 hours, at 00, 06, 12 and 18 UTC (Giusti, 2020).

ERA* has the objective to improve the ERAi forecasts by applying the scatterometer- based corrections from the already mentioned scatterometer constellation: ASCAT-A and ASCA-B (Advanced Scatterometer) onboard MetOp-A (Meteorological Operational) and MetOp-B EUMETSAT satellites, respectively, and OSCAT (OceanSat Scatterometer) onboard OceanSat-2.

Figure 4. Scheme representing the timeline for the generated forecasts. (Giusti, 2020)

3.2 Scatterometers

All three scatterometers are onboard polar orbiting sun-synchronous satellites. MetOp-A and MetOp-B were flying at 817 km altitude in 2013 (Krebs; NOAA Office of Satellite and Product Operations). The orbital period for MetOp is 101 minutes with about 14 orbits per day. The MetOp-A and Metop-B satellites share the same mid-morning orbit with MetOp-B phased 49 minutes apart from Metop-B (Klaes et al., 2013), both with local time at ascending node (LTAN) at 9:30pm. OceanSat-2 orbit period is 99 minutes with LTAN at noon (Kramer, 2002).

The Advanced Scatterometer (ASCAT) on board of MetOp satellites is a real aperture radar with vertically polarized antennas (Fig. 5). It transmits a long pulse to the surface and, after the backscattered signal is received, it is being spectrally analysed. The beams of antennae illuminate two 525 km swaths with a gap of 725 km between them. ASCAT-A and B resolution is 25 km grid. The resulting product is U10S in zonal and meridional wind components. The product accuracy should have less than 2 m/s of wind component standard deviation with a bias of less than 0.5 m/s in wind speed (OSI SAF/EARS Winds Team, 2019). As verified by Vogelzang et al., (2011), the ASCAT wind vector accuracy is about 1 m/s.

The OSCAT is a Ku-band conically scanning scatterometer system (“Description of OSCAT Data Products”). It uses a 1-meter dish antenna rotating at 20 rpm with two “spot” beams of about 25 km × 55 km size on the ground, a horizontal polarisation beam (HH) and a vertical polarisation beam (VV) that sweep the surface in a circular pattern (Fig. 6). The 1800-km-wide swath covers 90% of the ocean surface in 24 hours and represents a substantial improvement compared to side-looking ASCAT scatterometer. The atmosphere is not transparent to a Ku-band wavelength and the scatterometer precision degrades significantly even in case of moderate rains, which makes it necessary to filter such data during the quality control check. The resulting product is 50 km grid 10 m wind components that should have an accuracy better that 2 m/s RMS with a bias of less than

0.5 m/s in wind speed (Oceansat-2 Wind Product User Manual, 2013). As verified by Vogelzang et al. (2011), the Ku-band scatterometer wind vector accuracy is similar to that of ASCAT under rain-free conditions, although its resolution is coarser.

Figure 5. ASCAT wind scatterometer Figure 6: OSCAT wind scatterometer geometry geometry (ASCAT) (Oceansat-2 Wind Product User Manual, 2013).

The HSCAT product used for ERA* validation is a Ku-band scatterometer on-board of the Chinese ocean environment satellite Haiyang-2A. HY-2A satellite has a Sun- synchronous orbit at the altitude of 971 km and LTAN at 6am. HSCAT has a 1700-km swath and 25-km spatial resolution. As HSCAT uses Ku-band frequency, rain has a significant effect on overestimating HSCAT wind speed at low and moderate wind speeds (Zhao & Zhao, 2019). It has a comparable quality to that of other Ku-band systems (Lin et al., 2016; Zhu et al., 2014).

3.3 ERA*

ERA* generates and applies a correction based on the U10s scatterometer datasets to the ERAi NWP model. The correction is based on the temporally averaged difference between scatterometer and ERAi U10S at grid point (i,j) and time sample (t), as described in Eq. 1 (Trindade et al., 2020):

푀 1 푆퐶(푖, 푗, 푡 ) = ∑(푢푆퐶퐴푇푘 (푖, 푗, 푡) − 푢퐸푅퐴푖(푖, 푗, 푡)) (퐸푞. 1), 푓 푀 10푠 10푠 푡=1

푆퐶퐴푇푘 퐸푅퐴푖 where 푢10푠 and 푢10푠 correspond respectively to the scatterometer and ERAi zonal (or meridional) U10s component and k refers to the number of sensors used.

The correction can be calculated as an average over a certain temporal window, using one or several scatterometer systems. The resulting scatterometer correction (Eq. 1)

10 is applied to the model output at each forecast step using the Eq. 2, where i,j is a grid point in space and 푡푓 is the time point of the forecast model.

퐸푅퐴∗ 퐸푅퐴푖 푢10푠 (푖, 푗, 푡푓) = 푢10푠 (푖, 푗, 푡푓) + 푆퐶(푖, 푗, 푡푓) (퐸푞. 2)

The final ocean forcing product has a grid resolution of 12.5 km x 12.5 km and a temporal resolution of 3h (corresponding to the ERAi forecast steps) (Trindade et al., 2020).

4. Methodology

The ERA* product validation was performed over the Adriatic and the entire Mediterranean Sea for the 3 months period between February and April 2013, and, in particular, over the Adriatic Sea for two specific storm surge events that occurred during 2013, i.e., in the periods 7-14 February and 26 March - 2 April.

As already mentioned, two different ERA* configurations are assessed: The ERA* 1- day and the ERA* 3-day. In both cases, the scatterometer corrections are based on the following constellation: ASCAT-A, ASCAT-B and OSCAT.

The ERA* 1-day and 3-day ocean forcing products are spatially and temporally interpolated to HSCAT U10S acquisitions. Similarly, ERAi is also interpolated to HSCAT for the studied periods.

To run the metrics tests we filter the data for the Mediterranean and the Adriatic Seas applying a mask based on polygon coordinates. The polygon used for the Adriatic Sea includes a part of the Ionian Sea (see Fig. 7) as the Sirocco winds in the Ionian Sea may also influence the dynamics of the storm surge events in the Adriatic.

We then plot the collocated ERA*, ERAi and HSCAT data for the Mediterranean region to better analyse the evolution of the storm surge events and HSCAT quality control aspects (see example in Fig. 8). The graphical representation of the data helped to detect several errors in the collocations that had been further corrected.

In addition, ASCAT-A, ASCAT-B and OSCAT U10S wind fields are plotted to better understand the coverage of the area during storm surge periods 7-14 February and 26 March - 2 April. ASCAT-A and B winds are of higher resolution than OSCAT winds, but have more narrow swaths thus less area coverage (see Fig. 9). As mentioned before, the lack of the satellite coverage over the area may highly influence the ERA* quality, especially in the 1-day configuration.

Figure 7. The Polygon mask used to filter data in the Adriatic Sea. (Map source: Google Earth).

Figure 8. HSCAT wind vector fields for two consecutive ascending orbits several hours before the February storm surge event (February 11th, 2013).

To ensure that the collocations with HSCAT swaths for the ERA* 1-day, 3-day configurations and ERAi include the same points, we run an additional validation test comparing these datasets. Due to different versions of the collocation software used initially some differences were detected between the different collocation data sets. To avoid this, new collocations were generated for all data sets with the same version of the software.

Figure 9. Comparison between ASCAT-A (above) and OSCAT (below) coverage for two consecutive ascending orbit passes on February 11th, 2013.

To compare the ERA* 1-day, 3-day and ERAi products we run several metrics tests for the zonal (u) and meridional (v) U10S components as well as for the wind speed and direction components for the following data sets:

1. The entire Mediterranean Sea over the 3-month period February 1st – April 30th of 2013. 2. The Adriatic Sea with adjacent Ionian Sea (see Fig. 7) for the same 3 months. 3. The Adriatic Sea during the storm surge period including several days before and after the event (7-14 February 2013). 4. The Adriatic Sea during the storm surge event on March 26 – April 2.

The following metrics were calculated:

• Vector root mean square difference VRMS between NWP (i.e., ERA* or ERAi) and HSCAT:

푁 1 2 2 푉푅푀푆 = √ ∑((푢 − 푢 ) + (푣 − 푣 ) ) 푁 푖,퐻푆퐶퐴푇 푖,푁푊푃 푖,퐻푆퐶퐴푇 푖,푁푊푃 푖=1

• Mean vector difference MVD 푁 1 2 2 푀푉퐷 = ∑ √(푢 − 푢 ) + (푣 − 푣 ) 푁 푖,퐻푆퐶퐴푇 푖,푁푊푃 푖,퐻푆퐶퐴푇 푖,푁푊푃 푖=1 • Mean difference (bias) between NWP and HSCAT for the: o zonal and meridional components (u, v) o wind speed o wind direction • Standard deviation (σ) of the difference for: o zonal and meridional components o wind speed o wind direction • Correlation coefficients (R) for the: o zonal and meridional components o wind speed o wind direction • Number of data points used in the metrics

Besides the statistical results, we generate the scatterplots of NWP versus HSCAT winds to better assess their differences. These scatterplots correspond to zonal and meridional U10S components and for wind speed and direction components.

To evaluate the artefacts related to the gaps between scatterometer products we plot the (non-collocated) ERA* products in the Mediterranean for both the 1-day and 3-day configurations.

5. Results

5.1 3-month accumulated metrics for the zonal and meridional components over the Mediterranean and Adriatic Seas

First, we evaluate the performance of the ERA* and ERAi products for a larger period of 3 months between February 1st and April 30th of 2013. This way we can estimate how the products perform on a larger scale with average weather conditions in the basin. The overall number of measurements (about 300,000 in the Mediterranean and 37,000 in the Adriatic Sea) enable statistically significant results to assess both ERA* and ERAi products. Over the Mediterranean region (see Table 1), ERA* 3-day configuration outperforms ERAi, showing a reduction of VRMS values of about 3.5% and of MVD of about 3.8%. However, the 1-day configuration shows even better performance, reducing the VRMS values by 9% and MVD by 9.7%. In terms of error variance, the reduction is of about 16%.

TABLE 1. Statistical results of ERAi/ERA* 3-day/ERA* 1-day against HSCAT (see metrics definitions in section 4) for the zonal and meridian U10S components for the entire 3-month period, between 1 February and 30 April 2013, over the Mediterranean and Adriatic Seas. The total number of points for each area is given in parenthesis.

Mediterranean Sea (293,687) Adriatic Sea (36,897) Test ERAi ERA* 3-day ERA* 1-day ERAi ERA* 3-day ERA* 1-day VRMS 2.792 2.696 2.556 3.065 3.025 2.814 MVD 2.318 2.231 2.093 2.613 2.528 2.308 Bias (u) 0.079 0.001 -0.022 0.167 0.030 0.006 σ (u) 2.034 1.953 1.830 2.225 2.208 2.042 R (u) 0.949 0.952 0.958 0.920 0.921 0.932 Bias (v) -0.065 0.062 0.040 -0.367 -0.126 -0.120 σ (v) 1.911 1.857 1.784 2.069 2.063 1.933 R (v) 0.937 0.940 0.945 0.939 0.938 0.946

TABLE 2. Same as Table 1 but for the wind speed and wind direction components.

Mediterranean Sea Adriatic Sea ERAi ERA* 3-day ERA* 1-day ERAi ERA* 3-day ERA* 1-day Wind speed, m/s Correlation 0.864 0.874 0.889 0.832 0.835 0.863 Bias -0.531 -0.425 -0.202 -0.962 -0.715 -0.354 σ 1.801 1.725 1.655 1.98 1.911 1.752 Wind direction (winds over 4 m/s), degrees Correlation 0.944 0.949 0.953 0.952 0.951 0.952 Bias 4.771 2.234 0.590 5.154 2.653 1.077 σ 14.909 14.812 14.423 16.476 17.06 16.8

Table 1 shows that the in general (for all three products) the correlation for the zonal component (R (u)) is slightly higher that for the meridional one (R (v)), both for the Mediterranean and the Adriatic Seas. The ERA* 1 -day product shows the highest correlation of for the zonal (0.958) and for the meridional (0.945) components.

Table 2 represents the metrics for wind speed and wind direction for the Mediterranean Sea. There is an improvement in the accuracy for the ERA* product compared to that of ERAi, especially in its 1-day configuration. The latter has the highest correlation for both the wind speed and the wind direction components, and the biases in wind speed and direction are significantly reduced, as compared to ERA* 3-day and notably ERAi.

Fig. 10 shows the two-dimensional histograms of ERAi/ERA* versus HSCAT wind speed (left) and wind direction (right) for the Mediterranean Sea. The ERAi product tends to underestimate winds, especially those above 15 m/s. The ERA* 1-day product best agrees with HSCAT. In particular it has the largest correlation values and the smallest standard deviation (SD) values, both for the wind speed and the wind direction components. Note though that the wind direction correlation coefficients for all ERA products is quite similar (see Table 2).

As shown in Table 1, for the Adriatic Sea region, the ERA* 3-day configuration shows a similar performance to that of the ERAi product (see e.g., VRMS, MVD, and correlation scores) but presents a smaller bias than ERAi for both the zonal and the meridional components. The ERA* 1-day configuration shows an overall 8% (11.7%) reduction in VRMS (MVD) as compared to ERAi.

5.2 Metrics for the storm surge events in the Adriatic Sea

To evaluate the ERA* performance under storm surge conditions, we choose 2 storm surge events in 2013, one that caused a sea level rise of 143 cm at Punta Salute on February 12th and another of 125 cm on March 31st. In both cases, we take 7-day periods to assess the ERA product quality from 5 days prior to the sea level rise to 2 days after. For the February event there is no HSCAT coverage over the Adriatic Sea on February 10th, although the strong southeasterly winds started on February 11th. In both events we analyse over 3500 collocated data points, although during March event there was a slightly better HY-2A satellite coverage of the Adriatic Sea. For the February 7-14 period (see Table 3) the ERA* 3-day performance is similar to that of ERAi, although it has smaller biases for the meridional and zonal wind components. For the March 26 – April 2 period the ERA* 3-day configuration shows better performance, with 2.5% (3%) lower VRMS (MVD) values than ERAi.

Figure 10. Two dimensional histograms of ERAi (top), ERA* 3-day (middle), and ERA* 1-day (bottom) versus HSCAT, for the wind speed (left) and wind direction (right) components, over the Mediterranean Sea during the period February 1st – April 30th, 2013. N is the number of data; mx and my are the mean values along the x and y axis, respectively; m(y-x) and s(y-x) are the bias and the standard deviation with respect to the diagonal, respectively; and cor_xy is the correlation value between the x- and y-axis distributions. The colors (from cool to warm) represent 10%, 20%, 30%, ..., 90% of the maximum number from the densest bin.

TABLE 3. Same as Table 1, but only for the storm surge periods on February 7-14 and March 26 – April 2, 2013, in the Adriatic Sea.

February 7-14 (3553) March 26 – April 2 (3850) Test ERAi ERA* 3-day ERA* 1-day ERAi ERA* 3-day ERA* 1-day VRMS 3.338 3.344 3.193 2.840 2.773 2.705 MVD 2.737 2.703 2.540 2.426 2.351 2.301 Bias (u) 0.252 -0.094 -0.139 0.469 0.056 0.010 σ (u) 2.452 2.508 2.466 2.049 2.071 1.923 R (u) 0.933 0.926 0.926 0.937 0.936 0.943 Bias (v) -0.032 0.021 0.087 -0.707 0.055 -0.014 σ (v) 2.251 2.211 2.022 1.775 1.843 1.902 R (v) 0.950 0.948 0.956 0.893 0.883 0.875

TABLE 4. Same as Table 2 but only for the storm surge periods on February 7-14 and March 26 – April 2, 2013, in the Adriatic Sea.

February 7-14 March 26 – April 2 ERAi ERA* 3-day ERA* 1-day ERAi ERA* 3-day ERA* 1-day Wind speed, m/s Correlation 0.835 0.832 0.838 0.785 0.801 0.800 Bias -1.213 -1.058 -0.445 -0.963 -0.705 -0.442 σ 1.995 2.052 2.048 1.757 1.698 1.704 Wind direction (winds over 4 m/s), degrees Correlation 0.952 0.952 0.958 0.908 0.905 0.882 Bias 2.034 0.134 0.719 4.431 -1.054 -2.858 σ 14.851 15.153 15.170 16.084 17.160 17.833

The ERA* 1-day product outperforms ERAi for the February period, with 4.3% (7%) lower VRMS (MVD) values. For the March event, the VRMS (MVD) reduction is by 4.8% (5%). There is also a reduction in the ERA* 1-day zonal and meridional biases in both periods.

The SD values for the zonal and meridional components are in general higher for the February event than for that of March, which suggests a higher variability of the winds and in turn higher errors in the ERA products. For the wind speed component (see Table 4), ERA* products have smaller biases than ERAi in both periods, notably ERA* 1-day for the wind speed and ERA* 3-day for the wind direction.

As shown in Fig. 11, the February storm surge event was characterised by stronger winds (mean wind around 10 m/s) than the March event (7 m/s). For the February event the correlation coefficients for the wind speed and direction are generally higher that for the March event.

Figure 11. Same as Figure 10 but only for the wind speed component and for the storm surge events on February 7-14 (top) and March 26 – 2 April (bottom), 2013, in the Adriatic Sea.

ERAi generally underestimates winds above 10 m/s during both storm surge periods. While ERA* 3-day configuration shows a smaller underestimation of high winds, the ERA* 1-day outperforms the other ERA products, showing only a slight underestimation of the wind speed. However, for some grid points, ERA* 1-day shows a large underestimation (see off-diagonal accumulation in the lower part of Figure 11c and 11f). This is probably due to the already mentioned poor sampling issues, notably near the coast.

6. Discussion

As we have seen in the previous section, in the Mediterranean, ERA* generally has substantially better performance than ERAi, especially in its 1-day configuration. In terms of error variance (VRMS2), ERA* 1-day is about 16% lower than ERAi. As for the Adriatic Sea the performance of the ERA* 3-day product is comparable to that of ERAi, while the ERA* 1-day product shows a similar error variance reduction with respect to ERAi as in the Mediterranean. As previously mentioned in the introduction, the previous studies for the tropics and middle latitudes show that ERA* 3-day estimates have a better performance on a global scale than the ERA* 1-day configuration. The better performance of the ERA* 1-day product in the Mediterranean and Adriatic Seas may be due to the high wind variability in these regions, with important changes in wind speed and direction happening in a few hours. Local NWP biases are therefore blurred by rapidly evolving weather, leading to a reduced effectiveness of the scatterometer-based corrections over a few days. Shorter time windows, such as that used for ERA* 1-day, therefore become more effective. However, the ERA* 1-day product, as discussed previously, shows sometimes localized artefacts due to the relatively poor sampling over certain regions and times. Moreover, it is most sensitive to eventual satellite data gaps (due to, e.g., satellite manoeuvres). Such artefacts look like abrupt changes in wind speed and direction on the swath edges (see Fig. 12a, between Italy and Libya). By using larger temporal windows in the scatterometer- based corrections (e.g., 3 days), sampling issues are mitigated (see Fig. 12b). As shown in Fig. 12, the ERA* product, in both configurations, has sometimes artefacts near the coasts. Note that in the generation of ERA*, scatterometer winds near the coast are not used (i.e., those wind vector cells containing a small fraction of land are discarded). As such, the original ERAi product is kept in coastal areas, therefore producing similar sampling (swath-edge induced) artefacts as those reported for ERA* 1-day. As already mentioned, ERAi underestimates strong winds. Due to the high variability of the winds over the Mediterranean, the local wind biases become less persistent over time. As such, the local corrections applied over a 3-day window are less effective than those over a 1-day window. In turn, the ERA* 3-day configuration is less effective in correcting the high wind bias than the ERA* 1-day (see Fig. 10 and Table 2).

Figure 12. ERA* 1-day (a) and ERA* 3-day (b) wind field output, corresponding to the 6-hour forecast on February 21st 2013 at 18 UTC. Between Italy and Libya, an artefact (visible swath edge) can be seen in the ERA* 1-day (but not in ERA* 3-day), indicating poor scatterometer sampling in this area for that particular time window.

In this work we evaluated ERA* performance for 2 storm surge events that happened in February and March of 2013, and caused flooding in Venice. The February event was characterized by the presence of precipitation. Heavy precipitations can cause important errors in the data quality of the Ku-band scatterometers (e.g., OSCAT and HSCAT) (M. Portabella & Stoffelen, 2009; Zhao & Zhao, 2019). As for OSCAT, the quality flags are applied in the generation of the ERA* product, while for HSCAT, no flagging has been applied for verification purposes. Since the current quality control filters around 4-5% of the data, this omission may have a non-negligible impact in the verification scores, although this is expected to be small and similar to the verification of all the ERA products. Moreover, no apparent inconsistencies have been found in the HSCAT wind field test cases plotted in this report.

During these two storm surge periods, the ERA* 3-day configuration doesn’t show much improvement compared to ERAi product (see Table 3), while the 1-day configuration shows a larger correlation, notably for the wind speed component (see Table 4). As already mentioned, the ERA* 1-day has 4-5% lower VRMS than ERAi, i.e., about 9% error variance reduction. Fig. 13 shows the Sirocco winds just before the Acqua Alta events in the Adriatic basin. We can see that the ERA* 1-day wind field shows the best agreement with that of HSCAT. In particular, note the good agreement in the wind direction component, especially during the February event (Fig. 13c). We also can see that the ERAi product underestimates the wind speed (see Fig. 13a), while the ERA* 1-day configuration clearly corrects for such underestimation. This effect is also clear in Fig. 14, where the ERA* 1- day product shows a high wind area patch in the northern Adriatic (Fig. 14d), similar to that captured by HSCAT (Fig. 14a), but clearly underestimated by ERAi and ERA* 3-day (Figs. 14b and 14c). The February event has much stronger Sirocco winds up to 20 m/s, with mean winds about 10 m/s, while the March event has winds up to 15 m/s, with mean winds about 7 m/s (Fig. 11). During the March period the winds from the south-east direction are present during a longer period. This may explain why ERA* and ERAi products generally give better estimations during the March event (as compared to the February event), as the winds are more stable over time and don’t reach such extreme values as in February. If we look at the development of the storm surge events based on the data from the OSCAT scatterometer (with good coverage of the area during these periods), we can see that the strong sirocco winds were actually persistent only on 11 – 12th for the February event and during 3 days on March 28th – 30th with the wind changing its direction on March 31th. The metrics for those 2-3 days when the actual extreme events occurred show a substantially better performance of the ERA* product as compared to ERAi, especially for the 1-day configuration (see Table 5). For the February Sirocco wind forecast, the VRMS (MVD) is reduced by 20% (21%), while for the March the reduction is about 20% (23%). The biases for the zonal and meridional components are significantly reduced as well. Overall, the ERA* 1-day shows an error variance reduction of about 37% with respect to that of ERAi. As shown in Table 6, the ERA* 1-day product shows almost no wind speed and direction biases as compared to those of ERAi, and a better wind direction correlation. Fig. 15 shows how the biases in wind speed and direction are largely reduced in ERA* 1- day (compared to ERAi) on February 11th.

Figure 13. ERAi (left), ERA* 3-day (middle), and ERA* 1-day (right) wind fields (red arrows) collocated (interpolated) to the HSCAT data (black) over the Adriatic basin the day before the Acqua Alta events on February 12th (top) and March 31th (bottom), 2013.

Figure 14. HSCAT wind fields (a) for two consecutive ascending orbit passes on February 11th and collocated forecasts for ERAi (b), ERA* 3-day (c), and ERA* 1-day (d) configurations.

TABLE 5. Same as Table 1 but only for the Sirocco wind periods on February 11-12 and March 28- 30, 2013, in the Adriatic Sea.

February 11 – 12 (1073) March 28 – 30 (1397) Test ERAi ERA* 3-day ERA* 1-day ERAi ERA* 3-day ERA* 1-day VRMS 3.034 2.851 2.414 3.044 2.538 2.426 MVD 2.650 2.441 2.090 2.671 2.207 2.057 bias (u) 1.251 0.425 0.045 1.388 0.793 0.497 std (u) 1.918 2.165 1.781 1.895 1.772 1.653 R (u) 0.944 0.912 0.940 0.840 0.865 0.883 bias (v) -1.013 -0.557 -0.178 -1.016 -0.248 -0.317 std (v) 1.715 1.719 1.621 1.650 1.617 1.676 R (v) 0.963 0.961 0.966 0.848 0.858 0.851

TABLE 6. Same as Table 2 but only for the Sirocco wind periods on February 11-12 and March 28- 30, 2013, in the Adriatic Sea.

February 11-12 March 28 – 30 ERAi ERA* 3-day ERA* 1-day ERAi ERA* 3-day ERA* 1-day Wind speed, m/s Correlation 0.898 0.899 0.899 0.771 0.791 0.801 Bias -1.390 -0.953 -0.222 -1.372 -0.545 -0.395 σ 1.560 1.581 1.579 1.750 1.686 1.644 Wind direction (winds over 4 m/s), degrees Correlation 0.949 0.950 0.970 0.853 0.878 0.878 Bias 6.530 2.503 -0.310 6.362 4.208 1.545 σ 9.646 11.847 9.240 17.170 15.141 15.276

Figure 15. Vector (arrows) and wind speed (colours) difference between collocated ERAi (a) / ERA* 3-day (b) / ERA* 1-day (c) and HSCAT on 11 February 2013 at 16:20 UTC. The blue colours correspond to a wind speed underestimation by the NWP models.

As we can see from Fig. 16, in which the Sirocco winds on February 11-12 exceed 15 m/s, there is an important underestimation in the ERAi forecasts of up to 5 m/s (Fig. 16a), which is largely corrected by the ERA* 1-day configuration (Fig. 16c).

Figure 16. Same as Figure 11 but only for the Sirocco wind periods on February 11-12 and March 28-30 2013. In March, the Sirocco winds are not as strong as in February, although persist during a larger time period. The mean wind speed is about 6 m/s in March, while 12 m/s in February. In this case we also observe an important reduction of the ERA* biases, especially for 1-day configuration, as well as a higher correlation for wind speed. The correlation for the wind direction is the same for both ERA* products though. If we compare between these two periods, the NWP (ERAi/ERA*) models present higher overall correlation for the February winds, during which the magnitude of the Sirocco winds was about twice that in March. However, if we compare the results in these short periods to those of a larger 1-week period around the events, which include days with rapidly changing winds (see Tables 3 and 4), the results differ. The ERA* 3-day configuration shows better performance for the March period while in February its overall performance is comparable to that of ERAi. Overall, the ERA* 1-day product significantly outperforms the ERA* 3-day and ERAi products. In particular, it shows an error variance reduction with respect to that of ERAi of about 16% in the Adriatic, as well as in the entire Mediterranean region. Moreover, the error variance reduction can reach up to 37% during the Sirocco events.

7. Conclusions

Improving the accuracy of the ocean forcing forecasting is an important part of the coastal risks and hazards management, especially for low-lying, highly densed populated coastal areas worldwide and, in particular, for the area of Venice, whose exposure to the hazards of the historical heritage is of particular concern.

In this work, two configurations of a new scatterometer-based high-resolution ocean forcing product are assessed using independent scatterometer (HSCAT) observations with the aim to provide an optimized ocean forcing product for the Mediterranean region, and in particular, for improved predictions of sea level rise in the Adriatic Sea. The so-called ERA* is a corrected ERAi, using high-resolution ocean stress-equivalent winds (U10S) from a scatterometer constellation (ASCAT-A, -B, and OSCAT). In particular, the so-called ERA* 1-day and ERA* 3-day configurations which consist of ERAi corrected with averaged scatterometer/ERAi differences over 1 day and 3 days, respectively. Both ERA* configurations have been thoroughly analysed and compared to the original ERAi model output. Overall, the ERA* 1-day configuration outperforms ERA* 3-day and ERAi, showing a substantially lower error variance (against HSCAT) in the Mediterranean region (16% lower) and, in particular, during the storm surge events in the Adriatic Sea (up to 37% lower). Moreover, the large ERAi underestimation of the strong Sirocco events (up to 1.4 m/s underestimation) is substantially reduced in the ERA* 1-day product (0.2-0.4 m/s). This shows the potential of the ERA* 1-day product to significantly improve storm surge prediction.

The improvement of the ERA* accuracy depends on the coverage of the area by the scatterometer constellation used in the product. While ERA* 3-day has virtually no scatterometer gaps, ERA* 1-day generally has about 0.6% of open water gaps, a small but non-negligible percentage. In such gaps, the original ERAi winds are kept, leading not only to no improvement of the product in such areas but also to the presence of swath edge artefacts in the wind fields. Such artefacts are also present in coastal areas, now in both ERA* configurations, due to the land fraction flagging used in the scatterometer data. On the other hand, applying a 3-day temporal window correction for the periods of rapidly changing wind fields reduces the effectiveness of the scatterometer-based correction. Since for the analysed storm surge periods, the scatterometer constellation has relatively good coverage over the Adriatic Sea, especially by the OSCAT scatterometer, the ERA* 1- day configuration shows substantially better performance compared to that of the larger 3- day temporal window. The wind speed underestimation is one of the most important causes of the underestimation of the sea level rise forecasts during Aqua Alta events, which can cause errors up to 40 cm. As Venice is situated in a low-lying area, the errors of such magnitude result in important misinterpretation of the potential risks and impacts of the predicted events. Moreover, the reduction of such errors is key for a proper functioning of the flood alarm system and for an effective operational use of the upcoming MOSE protective system.

As the ERA-Interim reanalysis product is currently being replaced by the new ERA5 reanalysis, further developments of the ocean forcing product include the development of ERA5*. In comparison with ERAi, this new reanalysis uses a more recent version of the ECMWF model, with higher spatial resolution (31 km grid compared to 80km in ERA-Interim) and a larger number of observations from more sources used by the assimilation scheme (Guillory, 2017). The new ERA5* product based on the ERA5 reanalysis will be available on an hourly basis and the analysis time will be shifted to 06 and 18 UTC (ERA-Interim analysis was produced at 00 and 12 UTC). The model collocations with the scatterometers thus will be closer to the scatterometer acquisition time (0.5-hour difference compared to the 1.5-hour difference in the ERAi collocations). ERA5* will use the best combination of three scatterometers available for each year and the correction will have better quality as the scatterometer data will be reprocessed using the last geophysical model functions (GMFs). For the future validation of the ERA5* product against an independent scatterometer, the latter will be filtered by the following quality checks (flags): presence of rain (notably for Ku-band scatterometers), and proximity to the coast. This will give us more reliable metrics for the product evaluation. We expect that new ERA5* ocean forcing product will have an improved accuracy compared to that of ERA5 and the current version of ERA*.

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9. Acknowledgements

I would like to thank my supervisor Marcos Portabella for the overall guidance through this work and for making it possible for me to participate in the project in his department. For me it was a very enriching experience that grew my interest in the satellite products and numerical weather prediction models. Marcos is really great at motivating people and it was a big pleasure of working with him. He was very helpful at solving any kind of issues and dedicated a lot of his time to the improvement of this work.

I would also like to thank Ana Trindade who developed the product that was evaluated in this work. She gave me a technical input on the product, as well as shared the utilities for creating plots and running the metrics. During this project she had to rerun the product compilation and recreate several times the collocations, and I am very grateful for her time and advises.

My special thanks to Giorgi Khazaradze, who not only helped me with the formal aspects of this work, but also gave me his overall support and was one of the people who actually started my interest in the remote sensing field.

I am very grateful to my friend Matías Company Casas who gave me his complete support throughout this whole work, especially during the days of confinement, trying to give me the inspiration from over 600 km that separate us.

I also need to mention that neither this project, nor my degree studies in general would be possible without the support of my parents. I’m very grateful for the effort they’ve been doing all these years to make my dreams come true.