Meteorological Data Sources Research Articles

Análise comparativa da velocidade do vento e da temperatura do ar, entre dados gerados por reanálises meteorológicas e dados observacionais na região de Minas Gerais

The use of alternative sources of meteorological data has become increasingly common, making it possible to evaluate areas with no long or continuous series of meteorological data. In this context, the main objective of this study is to evaluate the performance of data series from the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) for the state of Minas Gerais and verify the possible use of them in the absence of data observations of air temperature and wind speed. The analyzes were performed by comparing observation data from 17 meteorological stations and reanalysis data of the CFSR and CFSV2 models. From the results of the statistical analysis, it is observed that the air temperature reanalysis data presented a good performance in the region of study. However, wind speed data show a weak correlation. These results show that the air temperature data from these reanalyses have the potential to be used as an alternative source of data. Further studies are suggested regarding the use of wind speed data from these reanalyses.

Comparison of the existing photovoltaic power plant performance simulation in terms of different sources of meteorological data

Detailed and reliable design of a photovoltaic (PV) power plant cannot be underestimated. In terms of energy production forecasts and legal issues concerning financing - simulation performance of the PV installation is a very important problem for both the investor and the bank. In order to simulate the performance of the PV system one has to choose proper meteorological database which would be the most reliable on the installation site. Since different meteorological databases have different measurements methods and analysis, choosing the best one is not a trivial problem. In order to find the best meteorological data source for PV simulation performance under eastern Poland conditions, PVSYST software has been used. Detailed project of an 1.39 MWp PV power plant located in Bordzilowka in the east of Poland was introduced into the software. Choosing different meteorological data bases, monthly and yearly energy production was established. The simulation results were compared with the real data obtained from the PV power plant.

Evaluation of Wind and Wave Simulations using Different Global Reanalyses

ABSTRACT Heo, K.-Y.; Ha, T.; Choi, J.-Y.; Park, K.-S.; Kwon, J.-I., and Jun, K., 2017. Evaluation of wind and wave simulations using different global reanalyses. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 99–103. Coconut Creek (Florida), ISSN 0749-0208. Sea surface wind plays an important role in oceanic phenomena and coastal environment. There are many sources of meteorological data available, both global and regional, and they differ not only in spatial and temporal resolution, but also in the number of observations included in the reanalysis and in the method of data assimilation used. The performance of the weather research and forecasting (WRF) model and its variational data assimilation system (WRFDA) with two global reanalyses (ERA-Interim and NCEP FNL) used as initial and lateral boundary conditions has been assessed during the period from January to December of 2014. ...

Assessment of the AERMOD dispersion model over complex terrain with different types of meteorological data: Tracy Power Plant experiment

The accuracy of air pollutants dispersion modelling results depends on the quality of the input data, including the representativeness of the meteorological data. The paper presents the results of the AERMOD model validation using data from Tracy Power Plant experiment (Nevada, USA) with various meteorological data sources, including WRF modelling system outputs. The highest efficiency of the AERMOD model performance was found using site-specific meteorological data and the results from the WRF model. In general, the AERMOD modelling system inadequately represents the concentration levels of tracer gas (SF6 ) at receptors situated below the emitter height in areas of complex topography.

Impacts of combining reanalyses and weather station data on the accuracy of discharge modelling

Reanalyses are important sources of meteorological data. Recent studies have shown that precipitation and temperature data from reanalysis present a strong potential for hydrological modelling, especially in regions with a sparse observational network. The objective of this study is to evaluate the impacts of the combination of three global atmospheric reanalyses – ERA-Interim, CFSR and MERRA – and one gridded observation dataset on the accuracy of hydrological model discharge simulations. Two combination approaches were used. The first one combined reanalyses and the observational database using a weighted average of the precipitation and temperature inputs. The second one consisted in using all meteorological inputs separately and combining the simulated hydrographs. The combinations were performed over 460 Canadian watersheds (representing regions with a low density of weather stations) and 370 US watersheds (representing regions with a higher density of weather stations). Results showed significant improvements in the simulated discharges for 68% and 92% of the Canadian watersheds for the input combinations and output combinations, respectively. Moreover, both approaches led to significant improvements in the simulated discharges for 72% of the US watersheds studied. For all watersheds where simulated discharges using observational data had a Nash Sutcliffe efficiency (NSE) lower than 0.5, the combination with reanalyses resulted in a median NSE increase of 0.3. This indicates that reanalysis can successfully compensate for deficiencies in the surface observation record and provide significantly better hydrological modelling performance.

Wind Energy Potentials and Its Trend in the South China Sea

Using a 30year (1976-2005) daily high-resolution reanalysis wind field dataset assimilated from several meteorological data sources, the wind speed and power characteristics of the South China Sea (SCS) were investigated using the Weibull shape and scale parameters. The region in general showed good wind characteristics. This is shown by high annual mean wind speed and power density values which are 5.93 m/s and 273.84 W/m2 respectively. The calculated annual mean wind power resource attributes the region to a relatively high potential site for large- scale grid connected wind turbine applications. The wind power ranged between 96.27 W/m2 in May and 527.03 W/m2 in December. Furthermore, spatio-temporal variations showed that strong trends in wind power exist in Luzon strait in the northern SCS and Xisha, Zhongsha, Luzon, Liyue bank in the central SCS which are evaluated as high wind potential regions and may be rated as locations excellent for installation of large wind turbines for electrical energy generation. Non-significant and negative trends dominate the southern SCS and may therefore, be suitable for small wind applications. The wind power density exhibited a significant increasing trend of 1.4 W/m2 yr-1 in the SCS as a whole throughout the study period. The trend is strongest (2.8 W/m2 yr-1) in winter.

Effects of local and global meteorological data on predicting blast noise levels

Predicting noise levels due to military blasts is important near training installations, since surrounding communities may be adversely affected. Prediction models, however, rely on atmospheric or weather data that is not readily available or easily attainable. In this study, blast noise level predictions 1-8 km from the source are made using two sources of meteorological data: publicly available data derived from weather stations that are tens of kilometers from a detonation site, and near-ground weather stations that are 2-6 km from the source. To predict the blast noise levels, statistical learning regression models are trained on each set of meteorological data and over 1000 measured blast events. Predictions from both meteorological data sources are compared to actual blast noise levels recorded during a field experiment by Valente et al. [2012], which were captured under a wide variety of atmospheric conditions. The root-mean-square errors for each model are compared, and the most important input pa...

Improvements in the predictions for the photovoltaic system performance of the Mediterranean regions

The performance assessment of photovoltaic (PV) systems is a complex process. Several meteorological data sources are available to evaluate the PV system generation. Different computing models can be applied to determinate the solar irradiance on the plane of the array (POA). The cooling effect of the PV module due to the wind speed should not be neglected. The present study may support several users to perform more accurate PV energy predictions, providing important suggestions to develop future PV system projects with more high reliability.Perez and Hay-Davies models for the computing of the irradiance on tilted surfaces are combined with three meteorological datasets, characterized by different monitoring period and meteo station location, to estimate the POA irradiance, the module temperature and PV energy output for a PV system located in the Mediterranean climate area. Prediction results are performed by the PVsyst tool and compared with the actual data. Simulations are carried out taking into account the wind effects on the PV module performance.Results demonstrate that the geographic features of the location, in which the weather station is located, have higher impact on the estimations of the PV system performance than the distance between the PV system and the meteo station. Perez and Hay-Davies models provide predictions of the PV energy and the module temperature with a difference up to 3% and 1% respectively. Yearly average wind speed in the range 2–4m/s fosters a cooling effect up to 3% higher than one due to the wind magnitude less of 2m/s, increasing the PV energy up to 1%.

Dispersion of positron emitting radioactive gases in a complex urban building array: a comparison of dose modelling approaches

A radiological assessment was carried out on the release of positron-emitting radioactive gases from a roof-level stack at a central London site. Different modelling approaches were performed to investigate the range of radiation doses to representative persons. Contributions from plume inhalation, gamma shine and immersion to effective dose were taken into account. Dry and wet surface deposition on the roof, and exposure from contamination on the skin of roof-workers, added only a mean 4.7% to effective dose and were neglected. A 1:200 scale model, consisting of the stack and surrounding buildings, was tested in a wind tunnel to simulate pollutant dispersion in the near-field region i.e. rooftop. Concentration field measurements in the wind tunnel were converted into effective dose, including for roof-workers installing glass cladding to the stack building. Changes in the building shape, from addition of the cladding layer, were investigated in terms of the near-field flow pattern and significant differences found between the two cases. Pollutant concentrations were also modelled using Air Dispersion Modelling System (ADMS) and the results used to calculate the effective dose using the same meteorological data set and source release terms. Sector averaged wind tunnel dose estimates were greater than the ADMS figure by approximately a factor of two to three. Different stack release heights were investigated in the wind tunnel and ADMS simulations in order to determine the best height for the replacement flue stack for the building. Other techniques were investigated: building wake models, modified Gaussian plume methods and uniform dilution into a hemispherical volume to show the wide variation in predicted dose possible with different approaches. Large differences found between simpler analytic approaches indicated that more robust radiological assessments, based on more complex modelling approaches, were required to achieve satisfactory estimates of radiation dose to representative groups in adjacent buildings and on the building rooftop.

Establishment of a global zenith troposphericdelay model using multi-source data

Radio space-based geodesy techniques suffer from atmosphere propagation delays, of which the ionospheric delay can be largely eliminated by iono-free carrier phase combination techniques, and then the tropospheric delay becomes the main error source. In general, we project the slant delay to zenith direction with mapping function in GNSS navigation and positioning, so modeling the zenith tropospheric delay (ZTD) is a common method to reduce the tropospheric influence on signal travelling. Currently, the commonly used tropospheric delay models are all based on single data source. In this paper we initially used multi-source ZTD data to construct a tropospheric delay model. The ZTD data used for modeling are gridded data from GGOS Atmosphere and IGS troposphere products respectively. The Global Geodetic Observing System (GGOS) Atmosphere is a project that aims to establish atmospheric models. It provides gridded data of global zenith delays with temporal resolution of 6 h (0:00, 6:00, 12:00, 18:00UTC) and spatial resolution of 2.5°×2° (Lon× Lat), which are derived from the reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). IGS troposphere products are derived from position solutions of IGS stations with a high temporal resolution of 5 min and a high accuracy. Firstly, we used IGS ZTD data to analyze the characteristics of short periodic variations (diurnal cycle and semidiurnal cycle). Then, on basis of this analysis and previous researches on ZTD periodic variations conducted by other researchers, we combined GGOS ZTD data and IGS ZTD data to establish ZTD model in accordance with the idea of direct ZTD modeling. We obtained the temporal parameters via cycle fitting for ZTD data and saved the parameters in the form of global Delaunay triangles. For the proposed ZTD model, the ZTD values can be estimated by the methods of height reduction and linear triangle interpolation. The cycle fitting residuals of GGOS ZTD data and IGS ZTD data show that the internal accuracy are 3.62 and 3.60 cm respectively, indicating that our function model with diurnal cycle and semidiurnal cycle can well describe the temporal variations of ZTD. We validated the multi-source data model with respect to ZTD grid data from 94 globally distributed IGS stations during 2013, which were not involved in modeling. The obtained results show that the global average bias and RMS are - 0.31 cm and 4.16 cm respectively. Compared to other tropospheric delay models, multi-source data model is significantly superior to GZTD model (bias: - 0.48 cm; RMS: 4.46 cm) which ignores the short cycles of ZTD and is better than the GGrid model without IGS ZTD involved in modeling. With an accuracy reduce of only 0.2 mm, the multi-source data model is comparable to the most accurate and complicated GPT2w model (bias: - 0.04 cm; RMS: 4.14 cm). At the same time, we validated the stability of multi-source model in space and time, the testing results show the good correction performance and reliability. In addition to the globally applicable convenience, the multi-source model also show a characteristic of regional augmentation, which can greatly improve the tropospheric correction performances in areas around IGS sites. With the gradual increase of regional Continuously Operating Reference Stations (CORS) and global IGS stations and the development of meteorological data sources, how to assimilate diverse ZTD data sources to establish a more comprehensive global multi-source tropospheric delay needs more detailed and further study.

Use of WRF result as meteorological input to DNDC model for greenhouse gas flux simulation

Continuous evolution of biogeochemical models developed in the past decades makes possible the more and more accurate estimation of trace and greenhouse gas fluxes of soils. Due to the detailed meteorological, soil, biological and chemical processes the modeled fluxes are getting closer and closer to the real values. For appropriate evaluation models need large amount of input data. In this paper we have investigated how to build an easily accessible meteorological input data source for biogeochemical models, as it is one of the most important input data sets that is either missing or difficult to get from meteorological networks. The DNDC ecological model was used for testing the WRF numerical weather prediction system as a potential data source. The reference dataset was built by numerical interpolation based on measured data. The average differences between the modeled output data using WRF and observed meteorological data in 2009 and 2010 are less than 3.98 ± 1.6; 8.68 ± 6.72 and 6.5 ± 2.17 per cent for CO2, N2O and CH4, respectively, for the test years. Generalization of the results for other regions is restricted, however this work encourages others to examine the applicability of WRF data instead of observed climate parameters.

MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable.

Multi-observation meteorological and GNSS data comparison with Numerical Weather Prediction model

In this study we have investigated a number of meteorological and GNSS data sources; our long-term plan is to construct an integrated model of neutral atmosphere with high temporal and spatial resolution. The integrated model will be based on a Numerical Weather Prediction (NWP) model with high spatial resolution and vertical information about various meteorological parameters. In the first step (covered by this study) we have compared the NWP model outputs with the reference data consisting of: high-quality ground-based meteorological observations, radiosonde profiles and GNSS products and we have established the accuracy of the provided parameters. The radiosonde and the GNSS observations have been found to be consistent (Zenith Total Delay (ZTD) discrepancy below 10mm), whereas the ground-based observations and soundings detect in NWP data: 1) a negative bias in air pressure, 2) a negative bias in water vapor partial pressure, and 3) a positive bias in temperature. The standard deviation of ZTD residuals of reference data and the NWP model is on order of 20mm. The NWP model COAMPS (Coupled Ocean/Atmosphere Mesoscale System) used in our study does not provide meteorological parameters with acceptable accuracy for positioning.

Surface Energy Fluxes Estimation Over the South Asia Subcontinent Through Assimilating MODIS/TERRA Satellite Data With In Situ Observations and GLDAS Product by SEBS Model

Evapotranspiration (ET) estimation is important to water resource management in the South Asia subcontinent, and remote sensing method is a good choice to get surface energy fluxes for ET estimation. However, the accuracy of regional atmospheric parameters plays a vital role especially for South Asia with few meteorological stations available. In this study, to seek a practical way to derive surface energy fluxes for ET estimation, we compare the performances of three methods based on SEBS model using different meteorological data sources (meteorological stations, GLDAS product and the combination of in situ observation, and satellite retrieval). The spatial distribution and value reasonability analysis of the estimates indicate that the three methods cannot provide reliable estimates because of the constraint of station number and GLDAS product accuracy. Correlation analysis finds that there is a good agreement between GLDAS air temperature product and in situ observations with the coefficient of determination (R2) above 0.4 for 5 days in 2008. According to the relationship, an integration method is proposed by recalibrating GLDAS air temperature with in situ observations with other atmospheric parameters supplied by GLDAS product, and the average values of the estimated LE calculated at 100 NDVI intervals are well correlated with NDVI with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> more than 0.695. The LE values are also within a reasonable value range with the maximum about 700 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> occurred at the full vegetation cover condition. The results indicate that the integration method is a practical way of obtaining surface energy fluxes and ET estimation for South Asia.

Gaussian process models for reference ET estimation from alternative meteorological data sources

Summary Accurate estimates of daily crop evapotranspiration (ET) are needed for efficient irrigation management, especially in arid and semi-arid regions where crop water demand exceeds rainfall. Daily grass or alfalfa reference ET values and crop coefficients are widely used to estimate crop water demand. Inaccurate reference ET estimates can hence have a tremendous impact on irrigation costs and the demands on U.S. freshwater resources, particularly within the Ogallala aquifer region. ET networks calculate reference ET using local meteorological data. With gaps in spatial coverage of existing networks and the agriculture-based Texas High Plains ET (TXHPET) network in jeopardy due to lack of funding, there is an immediate need for alternative sources capable of filling data gaps without high maintenance and field-based support costs. Non-agricultural weather stations located throughout the Texas High Plains are providing publicly accessible meteorological data. However, there are concerns that the data may not be suitable for estimating reference ET due to factors such as weather station siting, fetch requirements, data formats, parameters recorded, and quality control issues. The goal of the research reported in this paper is to assess the use of alternative data sources for reference ET computation. Towards this objective, we trained Gaussian process models, an instance of kernel-based machine learning algorithms, on data collected from weather stations to estimate reference ET values and augment the TXHPET database. Results show that Gaussian process models provide much greater accuracy than baseline least square regression models.

Numerical simulation of wave field in the South China Sea using WAVEWATCH III

Wave fields of the South China Sea (SCS) from 1976 to 2005 were simulated using WAVEWATCH III by inputting high-resolution reanalysis wind field datasets assimilated from several meteorological data sources. Comparisons of wave heights between WAVEWATCH III and TOPEX/Poseidon altimeter and buoy data show a good agreement. Our results show seasonal variation of wave direction as follows: 1. During the summer monsoon (April–September), waves from south occur from April through September in the southern SCS region, which prevail taking about 40% of the time; 2. During the winter monsoon (December–March), waves from northeast prevail throughout the SCS for 56% of the period; 3. The dominant wave direction in SCS is NE. The seasonal variation of wave height Hs in SCS shows that in spring, Hs ≥1 m in the central SCS region and is less than 1 m in other areas. In summer, Hs is higher than in spring. During September-November, influenced by tropical cyclones, Hs is mostly higher than 1 m. East of Hainan Island, Hs>2 m. In winter, Hs reaches its maximum value influenced by the north-east monsoon, and heights over 2 m are found over a large part of SCS. Finally, we calculated the extreme wave parameters in SCS and found that the extreme wind speed and wave height for the 100-year return period for SCS peaked at 45 m/s and 19 m, respectively, SE of Hainan Island and decreased from north to south.

A chrysophyte stomatocyst-based reconstruction of cold-season air temperature from Alpine Lake Silvaplana (AD 1500–2003); methods and concepts for quantitative inferences

Relatively little is known about past cold-season temperature variability in high-Alpine regions because of a lack of natural cold-season temperature proxies as well as under-representation of high-altitude sites in meteorological, early-instrumental and documentary data sources. Recent studies have shown that chrysophyte stomatocysts, or simply cysts (sub-fossil algal remains of Chrysophyceae and Synurophyceae), are among the very few natural proxies that can be used to reconstruct cold-season temperatures. This study presents a quantitative, high-resolution (5-year), cold-season (Oct–May) temperature reconstruction based on sub-fossil chrysophyte stomatocysts in the annually laminated (varved) sediments of high-Alpine Lake Silvaplana, SE Switzerland (1,789 m a.s.l.), since AD 1500. We first explore the method used to translate an ecologically meaningful variable based on a biological proxy into a simple climate variable. A transfer function was applied to reconstruct the ‘date of spring mixing’ from cyst assemblages. Next, statistical regression models were tested to convert the reconstructed ‘dates of spring mixing’ into cold-season surface air temperatures with associated errors. The strengths and weaknesses of this approach are thoroughly tested. One much-debated, basic assumption for reconstructions (‘stationarity’), which states that only the environmental variable of interest has influenced cyst assemblages and the influence of confounding variables is negligible over time, is addressed in detail. Our inferences show that past cold-season air-temperature fluctuations were substantial and larger than those of other temperature reconstructions for Europe and the Alpine region. Interestingly, in this study, recent cold-season temperatures only just exceed those of previous, multi-decadal warm phases since AD 1500. These findings highlight the importance of local studies to assess natural climate variability at high altitudes.

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

AbstractSkilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley &amp; Sons, Ltd.

The archive and library of the former Italian Central Office for Meteorology and Climatology

Abstract. The Paper Archive and Library of CRA-CMA (Agricultural Research Council – Research Unit for Climatology and Meteorology applied to Agriculture) are an important source of meteorological data for climate research. CRA-CMA's Paper Archive gathers a collection of about 850 historical meteorological datasets. Among them, 40 are nowadays still in progress, 260 are more than thirty years long and 20 exceed one century. Moreover, the specialized Library of CRA-CMA gathers several publications containing meteorological data from many Italian and foreign observatories and an important collection of scientific journals and historical books on Atmospheric Sciences, Geophysics and Agrometeorology published both in Italy and abroad and dating from the second half of the sixteenth century. Even if input data for climate models are generally based on 30 yr long datasets, nevertheless longer observational series (up to 50 or 100 yr) are a key element to better understand the climate system behavior. Until today, the library described in this paper is a CRA-CMA heritage almost unknown to the international scientific community.