dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, we build an algorithm generating synthetic tracks directly from CMIP models, expose the biases in CMIP5 datasets and propose a correction module based on  Vrac et al. (2012). Third, we bridge the gap between climate data and damage modeling by using the physical asset values from  Eberenz et al. (2020b)  and computing the damage along cyclone tracks using the region-specific damage functions designed in the CLIMADA project. Combining open data sources and methodologies allows us to propose a complete bottom-up integrated physical risk assessment model for tropical cyclones presented in Fig. 1 : the Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA). The process is the following. The cyclones are initiated with spatial and seasonal distribution estimated on the International Best Track Archive for Climate Stewardship (IB-TrACS) database, similarly to  Bloemendaal et al.
This step requires us to extract the local physical asset values and aggregate them on tiles of length defined proportionally to the average radius to the maximum wind (approximately 50 km) along the cyclone path. Summing the losses along tracks for each year and for each country allows us to establish a national assessment of the damage generated by tropical cyclones under various assumptions concerning future adaptation to cyclone risk. CATHERINA aims to provide country level estimates for future damage from tropical cyclones, consistent with climate model projections, with mainly economic and financial applications in mind. Examples of applications include estimating the impact of cyclones on the creditworthiness of government debt, providing a physical risk module for integrated assessment models, creating physical risk scenarios for stress testing the resiliency of the financial system at a country level and at a global level, etc.
Given this aggregate country level analysis objective, our model is certainly a simplification compared to state-of-the-art cyclone dynamics models, does not aim for precise prediction of individual cyclone tracks and does not integrate a bottom-up description of damage to individual assets. At the same time, our paper improves earlier studies of cyclone damage on the aggregate level. For example, compared to  Mendelsohn et al. (2012) , our model uses several climate scenarios with state-of-theart bias correction as well as shared socioeconomic pathways (SSPs) to project population and regional domestic products and is based on precise physical asset value distribution. The paper is organized as follows. Section 2 first describes the datasets used to fit the model on ERA5 and to generate synthetic cyclones based on both the ERA5 and CMIP5 model datasets (Sect.
2. 1). Next, we present the statistical calibration process and the details of the thermodynamic instrumental variables (Sect. 2. 2 to 2. 4). Section 3 presents the dataset used to represent physical asset exposure and the SSP framework used to project their future value. Section 4 recalls the calibration methods implemented in the CLIMADA environment to fit the regional damage functions, defines the parameters of these functions using  Eberenz et al. (2021)  and applies them along the synthetic tracks to study the distribution of national annual damages. Section 5 explores the https://doi. org/10. 5194/gmd-15-8001-2022 Geosci. Model Dev. , 15, 8001-8039, 2022 properties of the synthetic tracks produced with the ERA5 and seven CMIP models, assesses climate uncertainty and introduces the bias-correction module in the context of changing climate conditions.
ERA5 reanalysis Climate reanalyses describe the historical climate conditions, obtained by assimilating all available observations into the models. They provide numerical estimates of atmospheric parameters (e. g. , air temperature, pressure and wind) at different altitudes/pressure levels and surface parameters (such as rainfall, soil moisture content, ocean-wave height and sea surface temperature) on a single level. We use reanalyses to calibrate the cyclone generation algorithm based on the most realistic available estimates of climate variables. We use the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) to fit the CATHERINA model 2  (Hersbach et al. , 2020). This dataset covers the Earth on a 30 km grid (∼ 0. 25 • ) and resolves the atmosphere using 137 levels from the surface up to a height of 80 km.
Historical cyclone tracks (IBTrACS) We use the IBTrACS database  (Knapp et al. , 2010). 4 This database provides information on past cyclone tracks at 3 h frequency. We remove the events classified as disturbance or extratropical and do not consider the South Atlantic basin (see Fig. A1  for more information). Climate reanalysis availability requires us to focus on post-1980 cyclones, which reduces the database to 4574 cyclones. In the context of an integrated damage assessment, to focus on the events that have a potentially substantial impact on assets, we select only tropical cyclones with maximum wind speed exceeding 35 m s -1 , obtaining 2966 on the full database and 1451 focusing on tropical cyclones between 1980 and April 2020. In Fig. 2 , we plot the central pressure along each cyclone life.
This graph suggests that the cyclone phases are fully represented in the 2 Climate data are available at the Copernicus Climate data store: https://cds. climate. copernicus. eu/ (last access: 14 October 2022). 3 We retrieve both MSLP and RH to define P env and RH env  (Holland, 1997)  away from the center in the reanalysis because tropical cyclone thermodynamic potential intensity -through thermodynamic efficiency and moist entropy (Eqs. 7 and 6) -arises from the deviations from the normal conditions. Monthly averaging may smooth values so that the data extracted along historical tracks may not represent the conditions at the time of cyclone passage. Therefore, using monthly means, this translation is mainly done for reasons of theoretical coherence. In future studies, this model will be applied with higher temporal resolution, and performing this translation would be more important.
In the present version of our paper, because the CMIP5 projections of the sea level temperature were only available at monthly frequency in the CDS, we chose to perform the exercise using monthly data to illustrate our approach. 4 See http://ibtracs. unca. edu/ (last access: 14 October 2022) for a browser of the data and https://www. ncdc. noaa. gov/ibtracs/index. php?name=ib-v4-access (last access: 14 October 2022) for the full dataset. database, i. e. , from the genesis to dissipation. The northern Indian basin has the lowest number of reported events with wind speed above 35 m s -1 (50 compared to 291 for the eastern Pacific, 185 for the North Atlantic, 305 for southern India, 158 for the South Pacific and 515 for the western Pacific) with variable reporting quality, which explains the more erratic shapes of the central pressure.
We use the parameters given in  Bloemendaal et al. (2020) , i. e. , λEP = 14. 5 for the eastern Pacific, λNA = 10. 8 for the North Atlantic, λNI = 2. 0 for northern India, λSI = 12. 3 for southern India, λSP = 9. 3 for the South Pacific and λWP = 22. 5 for the western Pacific. The parameters would have been smaller if estimated using our filtered database of tropical cyclones with wind speeds above 35 m s -1 : λ35 EP = 7. 31, λ35 NA = 4. 43, λ35 NI = 1. 6, λ35 SI = 6. 81, λ35 SP = 4. 00 and λ35 WP = 11. 86. However, we maintain the original parameters used in STORM to take into consideration the fact that some events will be generated in conditions not favorable for the development of cyclones and cleared out of the database. The number of cyclones making landfall being critical for damage estimation  (Hall and Jewson, 2007; Lee et al.
WPRs We describe the cyclone intensity through its central pressure P c t , which is linked to the maximum wind through an empirical relationship. Let V t be the maximum 10 min sustained wind speed (m s -1 ) 5  of the cyclone at time t. This maximum wind is observed around 50 km away from the storm center on average 6  and reported in the IBTrACS dataset for historically observed cyclones. The WPR is calibrated separately for each basin and takes the following form: V t = a P env (x t , y t , t) -P c t b , (3) where P env (x, y, t) is the MSLP extracted 500 km away from the eye location at this time in ERA5 and P c t is the central pressure extracted from IBTrACS. This relationship is illustrated in Fig. 6 , and the parameters a and b are fitted on ERA5 and IBTrACS data using nonlinear least squares.
Local thermodynamic MPI We compute the local thermodynamic MPI following the thermodynamic relationships defined in  Holland (1997). This is particularly relevant in the context of a national damage assessment under a changing climate. Greenhouse gas emissions not only warm up the oceans, but also cool down the lower stratosphere  (Butchart et al. , 2000; Forster et al. , 2007; Ramaswamy et al. , 2006). Thus, the tropopause temperature -that is, the temperature corresponding to a pressure of 50 hPa or to an altitude of approximately 20 km available in a multi-level CMIP dataset (T tropo ) -must be included in the modeling of the intensification process. Indeed, the thermodynamic efficiency factor E t proportional to the difference between the tropopause and sea surface temperatures plays an essential role in the determination of the central pressure of tropical cyclones.
at environmental conditions: q c t = 3. 08 P c t-1 exp (SST(x t , y t , t) -273. 15) SST(x t , y t , t) -29. 65 , (8) q env t = 3. 08 • RH(x t , where (x t , y t , t) are the coordinates of the eye defined in Eqs. (  1 ) and (2), SST(x t , y t , t) and T tropo (x t , y t , t) are, respectively, the sea surface and tropopause temperatures, R d = 287. 058 J kg -1 K -1 is the specific gas constant for dry air, MSLP(x t , y t , t) is the mean local sea level pressure, RH(x t , y t , t) is the near-surface relative humidity at 2 m extracted from the monthly dataset of ERA5 climate reanalysis or CMIP climate models. f (y t ) = 2ω sin(y t ) is a Coriolis parameter depending on the latitude, r env is the distance between the eye and the area under regular conditions (fixed at 500 km), q env and q * c , respectively, are the specific humidity at environmental conditions and at saturation, i.
Depression dynamics The evolution of the central pressure depending on the intensification factor Y t (which is defined differently in our approach and in STORM, as we explain below) is described by the following autoregressive stochastic equation  (James and Mason, 2005) : P c t = c 0 + c 1 P c t-1 + c 2 e -c 3[ P c t -Y t ] + ε P t , (11) ε P t ∼ N (0, σ P c ). ( 12 ) This relationship channels the effect of global warming, affecting the thermodynamic potential constructed with climate projections, on the cyclone intensity. Thus, the incremental variation of the central depression of the cyclone is linked to the difference between the central pressure at time t and the potential available in the environment. The intensification module of CATHERINA is inspired by STORM  (Bloemendaal et al.
Consequently, even if some thermodynamic processes have been simplified in our approach, it is still a step forward with respect to the existing state of the art in the field of integrated assessment modeling for climate impact analysis. Indeed, our approach is easy to implement, more sophisticated in terms of processes included than most existing IAMs, has low bias due to our state-of-the-art bias-correction module described below, and can integrate any CMIP simulation with a limited set of available variables (only a few vertical levels, some only available at a monthly timescale, with some variables not always available). 3 Exposure in the shared socioeconomic pathways 3. 1 Physical asset exposure  Eberenz et al. (2020a)  present a methodology to downscale physical asset values on a high-resolution grid using a combination of nightlight intensity, population data, and global country indicators and make their dataset fully available  (Eberenz et al.
, 2020b). These estimates of physical asset values are based on the light intensity L i -from nighttime lights of the Black Marble 2016 annual composite of the VI-IRS day-night band (DNB)  (Román et al. , 2018 ) in 2016 at the 15 arcsec resolution -and the population P pix per pixelhttps://doi. org/10. 5194/gmd-15-8001-2022 Geosci. Model Dev. , 15, 8001-8039, 2022 A pix = A tot L i • P pix N pix i L i • P pix i. ( 14 ) The physical asset value is expressed in USD as of 2014. Using this dataset in the future requires correction (either simulated or reported damages) for inflation using, for instance, the consumer price index (available at https://fred. stlouisfed. org, last access: 14 October 2022). This method for allocation of national assets has limitations. For example, the distribution of assets near the coast, 7 Produced capital, comprehensive global estimate of produced capital stock, i.
Dynamic projection of local exposure in SSPs To estimate future exposures along the cyclone tracks in each scenario, we use the downscaled estimation for the exposed wealth  (Eberenz et al. , 2020b)  and the coefficients representing the change between the current state and the future scenario in the framework of the shared socioeconomic pathways  (O'Neill et al. , 2014 (O'Neill et al. , , 2017;; Jones and O'Neill, 2020). The local physical exposure at the coordinates (x, y) at time t in a region j in scenario k is defined as follows: (x, y, j, k, t) = (F cap GDP (j, k, t)) α 1 Global macro factor • (F pop (x, y, k, t)) α 2 • L P (x, y) Local factor , (15) where L P (x, y) is the local population density from  Eberenz et al. (2020b) , and the factor F cap GDP is the projected GDP per capita growth for each region: F cap GDP (j, k, t) = GDP(j, k, t)/GDP(j, t = 2020) P (j, k, t)/P (j, t = 2020) , ( 16 ) where P is the total population of the region retrieved from the SSP database  (Riahi et al.
). Therefore, CATHERINA relies on regional damage functions calibrated by  Eberenz et al. (2021)  on wind speed along the cyclone track (IBTrACS) and reported damages in  Guha-Sapir et al. (2018). Damages provoked by tropical cyclones can be related to several sub-perils. While 40 % of cyclone damages are directly wind-related, another 40 % can generally be attributed to storm surge, and the rest of the damage is generated by heavy precipitation. However, CATHERINA does not distinguish these sub-perils but uses a statistical relationship to estimate the regional damage induced by a cyclone from a proxy variable given by the maximum wind speed. Indeed, the wind speed is the proxy used in the Saffir-Simpson hurricane wind scale to define the intensity of a cyclone. The damage function is fitted on multiple events from the total damage reported in the global disaster database EM-DAT (Guha-Sapir et al.
, 2018) (available at http://www. emdat. be (last access: 14 October 2022) This database, used in most studies   A5. on the topic, accounts for the total reported damage (sum of all sub-perils) and does not distinguish damages from subperils. Filtering the database by subtype "tropical cyclone" allows us to extract 1855 tropical cyclones in the period between 1980 and 2021, among which 1101 events have a reported total damage cost in USD (see Fig. 17 ). In terms of damage, tropical cyclones are, using the full set of observations from 1980 to 2021, the most damaging events reported (see Fig. 16 ). The database includes a start date field (day, month and year) allowing us to map 455 events, with the events reported in IBTrACS using the start year and month and country. 11 We use this database to validate our simplified estimation process.
These important biases and uncertainties may be mitigated in the latest launch of the models on the occasion of CMIP6  (Gusain et al. , 2020). tween the value produced by the model and the one produced by the ERA5 reanalysis. Figure  22  displays the average relative errors and shows that a 2 % relative error in the description of the maximum potential intensity can lead to more than 20 % error in the description of the implied wind compared to the result obtained with ERA5. This figure illustrates the efficiency of bias correction in the historical period. Indeed, on average, the CDF-t correction technique clearly reduces the error between the MPI estimated with climate reanalysis and the one computed from modeled climate data as well as (more importantly) the error in the description of the maximum wind speed.
, 2012, Fig. 3 , for example), socioeconomic change leads to wider differences than climate change. Figure  24  represents the expected value of future damage, and Table  4  provides the expected values (together with standard errors) along with the 50th, 66th and 95th percentiles of the global annual damage distribution in the simulations under various assumptions of future exposure growth (values of α 1 and α 2 ). In the discussion below, CATHERINA simulations of 300 representative years of synthetic tracks using ERA5 data over the 1980-2010 period are used as a baseline. The choice α 1 = α 2 = 1 assumes linear increase in exposure with respect to both GDP per capita and local exposed population. Using α 1 = 1/3, which is close to the value estimated in  Bakkensen and Mendelsohn (2019) , allows us to  account for possible future adaptation.
Conclusions This paper proposes a structural framework to generate synthetic storms based on large-scale climate data and to produce simulations of annual cyclone damages per country. We show that, when used with reanalysis data and CMIP5 models over the historical period, our method produces tracks consistent with historical observations. The synthetic tracks generated with our model have several applications. The first one is in natural disaster risk management to calibrate adaptation measures. For this purpose, the track-generation algorithm may be enhanced, for instance, by including dependency in the latitudinal and longitudinal incremental displacement, coupling with a meteorological forecasting model, or including ground topography to model the cyclone displacement over land. Another major field of application is to climate financial risk management, where this scenario-based event database can be used to evaluate physical risks and compute portfolio exposures.
This would require us to better define asset level vulnerabilities. The dataset used for the examples of this paper, based on low-resolution data and including a limited number of simulations, may not be accurate enough to properly calibrate adaptation measures. However, we believe that the framework presented here may be used to project a dense set of trajectories, compute expected damage and damage percentiles over the next decades and measure the investment required for adaptation and mitigation measures in the next 50 years. This work also reflects a practical exercise not carried out until now, of cross-referencing the latest datasets developed, putting into perspective both the socioeconomic and climatic development hypotheses and carrying out a bottom-up, rather than top-down, damage calculation.
Acknowledgements. The authors are very grateful to  Florian Raymond ,  Thierry Roncalli ,  Takaya Sekine  and  Lauren Stagnol  for helpful comments. Financial support. This work contributes to the  Energy4Climate Interdisciplinary Center (E4C) of the Institut Polytechnique de Paris  and the  Ecole des Ponts ParisTech , supported by the  3rd Programme d'Investissements d'Avenir  ( ANR-18-EUR-0006-02 ). Peter Tankov  was financially supported by the  FIME Research Initiative. This work was also supported by  Amundi Asset Management. Review statement. This paper was edited by  Jinkyu Hong  and reviewed by three anonymous referees.
T. Le Guenedal et al. :  CATHERINA 1. 0 model compatible with CMIP climate data Code and data availability. Code is available from the E4C data hub at https://www. e4c. ip-paris. fr/#/fr/datahub/ projects (last access: 14 October 2022) and Zenodo at https://doi. org/10. 5281/zenodo. 5645516 (Le  Guenedal et al. , 2021). The deposit includes R scripts with functions, a fitting script, the parameters of the fitting of all the relationships on ERA5, historical and future synthetic tracks, and a sample of simulated annual damage per country and scenario. We included a user guide for data exploration and a quick-start guide.
Because of the time slicing of the CMIP5 climate data available in the climate data store, we launch the models in 25 consecutive years over this period: 2070-2095 for IPSL, BCC, NCC and CCCMA, 2075-2100 for GISS and 2085-2095 for INM (only a 10year slice of climate data is available on the CDS for this model). We repeat this process, changing the seed 12 times to obtain 300 representative years in each model (except for INM, for which we only have 120 years). https://doi. org/10. 5194/gmd-15-8001-2022 Geosci. Model Dev. , 15, 8001-8039, 2022.
FRB 20200413A This is the first FRB disco v ered by MeerTRAP. It was detected in the incoherent beam (IB) on UT 2020 April 13 during commensal observations of the pulsar timing experiment at MeerKAT (MeerTime;  Bailes et al. 2020 ). The FRB was only detected in the IB, with no detections in the CB, suggesting that the FRB was outside the more sensitive CB tiling pattern. The source was detected in real-time pipeline with a S/N of 9. 1 and a DM of 1988 pc cm -3. The S/N and the DM were then optimized to values of 14 and 1990. 05 pc cm -3 , respectively, using a custom made S/N-DM optimization code MTCUTILS. 4 The FRB spans the entire bandwidth of the MeerKAT L -Band receiver (856-1712 MHz) and shows broadening of the pulse width at lower frequencies, probably due to the increase in the intra-channel dispersion smearing ( t DMsmear 20 ms at a frequency of 856 MHz for 1024 channels) and scattering.
FRB 20201123A The dynamic spectrum of FRB 20201123A sho ws e vidence for scattering. To fully characterize the scattering, we divided the data into four sub-bands, each 214 MHz wide with a compromise between bandwidth and the S/N of the burst in each sub-band. We assume the scattering is caused by a thin scattering screen between the source and the observer that leads to an exponential tail in the resulting profile (see  Chawla et al. 2021  , and the references therein). Then, we used the SCAMP-I software suite  (Oswald et al. 2021  ) to fit the burst profile at each frequency with an exponentially modified Gaussian. 5 The code simultaneously fits for the scattering as well as the correction needed to the DM of the burst such that the peak of the burst under this model is aligned correctly in time after correcting for scatter broadening.
For a total of 60 dishes used in the observation, we estimate G CB 2. 75 K Jy -1 and G IB 0. 35 K Jy -1. We assume here that the beamforming efficiency was close to 1. Hence, for a S/N of 45 in the IB, we expect S / N image of ∼4. 4 in a 8 s integration radio image. The MALS data were processed using the automated radio telescope imaging pipeline (ARTIP). The details of ARTIP and data processing steps are provided in  Gupta et al. ( 2021 ). In short, we excluded the edge frequency channels and applied a RFI mask to exclude the strong persistent RFI in the L -band. The data were then flagged and calibrated using the ARTIP-CAL package. The calibrated data were then processed using ARTIP-CONT to perform wide-band continuum imaging. For this the calibrated target, source data were averaged in frequency per 32 channels ( ∼0.