High resolution re-analysis for the Baltic Sea region during 1965–2005 period

Luhamaa, Andres; Kimmel, Kaarel; Männik, Aarne; Rõõm, Rein

doi:10.1007/s00382-010-0842-y

Cited by 34 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another important point is that higher resolution images are typically more expensive and the revisiting capability of higher resolution sensors is considerably lower compared to the medium resolution sensors like MERIS and MODIS. Also, the reliability of present reanalysis methods of meteorological variables seems to drop when the pixel size decreases (Luhamaa et al, 2011).…”

Section: Discussion and Further Perspectivesmentioning

confidence: 97%

“…The daily measured Q values in Tõravere were extrapolated to all pixels in Estonia by using the daily total cloudiness estimate for the pixel as obtained from the BaltAN65+ regional reanalysis database for the Baltic Sea region (Luhamaa et al, 2011). To calculate the daily total value of Q at any pixel location in Estonia (given by its day of the year (DOY) number), the following relation was used:…”

Section: Description Of the Est_pp Modelmentioning

confidence: 99%

“…of the order of magnitude of 100 km). Recently, for the Baltic Sea region a high-resolution reanalysis (11 × 11 km 2 ) database BaltAn65+ MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation was created (Luhamaa et al, 2011), which is much more suitable for NPP models that typically run at 1 km 2 resolution. Below, a LUE type GPP/NPP model named EST_PP is described and applied to calculate GPP and NPP of Estonian land surface, where the main attention is paid to the forests.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

et al. 2012

Self Cite

View full text Add to dashboard Cite

Abstract.A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km 2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km 2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather prediction model. The land cover map of Estonia needed for the model was derived using DMCii (Disaster Monitoring Constellation International Imaging) SLIM-6-22 (Surrey Linear Imager -6 channel -22 m resolution) images and ancillary information. The EST_PP model was run for the period from years 2003 to 2011. The results of GPP and NPP simulation are compared with the available global MODIS (Moderate Resolution Imaging Spectroradiometer) GPP/NPP product and with the Estonian statistical data on yearly volume increment in forests and on yield of agricultural crops. The NPP simulation results on coniferous and deciduous forests are compared with the data from tree ring analyses from different counties. These comparisons show us that the simulated country average yearly NPP values for Estonian forests agree reasonably well with the indirect estimates from other sources, taking into account the rather high uncertainty of the model predictions, uncertainty of forest inventory-based estimates and limited representativity of existing tree ring data. However, problems arise with the ability of present versions of EST_PP and MODIS NPP models to adequately simulate the regional differences of productivity and of variability of productivity in different years. The model needs some modification and the basic LUE principles to be tested in Estonia. Nevertheless, the MODIS NPP and EST_PP models offer additional possibilities to map yearly productivity and carbon sequestration by Estonian vegetation. There is a perspective to add the model-simulated NPP values into the national inventory datasets.

show abstract

Section: Discussion and Further Perspectivesmentioning

confidence: 97%

Section: Description Of the Est_pp Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…Over sea grid-points the geopotential fields used to plot the model orographies show "spectral ripples", which are a reflection of the fact that the ERA-Interim and HARMONIE models are defined in spectral space. Several regional reanalyses have already been produced including, for example, the Arctic Reanalysis ASR (Bromwich et al, 2010(Bromwich et al, , 2000(Bromwich et al, -2010, the Baltic Sea reanalysis (Luhamaa et al, 2011(Luhamaa et al, , 1965(Luhamaa et al, -2005, COSMO-REA6 (Bollmeyer et al, 2015(Bollmeyer et al, , 1997(Bollmeyer et al, -2004, the HIRLAM reanalysis (Dahlgren et al, 2016(Dahlgren et al, , 1979(Dahlgren et al, -2014, the North American Regional Reanalysis (Mesinger et al, 2006(Mesinger et al, , 1979(Mesinger et al, -2003 and the South Asian Regional Reanalysis (Kar et al, 2016(Kar et al, , 1998(Kar et al, -2002 dress this, we have run the HARMONIE-AROME canonical configuration of the ALADIN-HIRLAM system on a 2.5 km horizontal grid for the 35-year period 1981-2015 forced by ERA-Interim lateral boundary conditions, with the aim of improving our knowledge of surface parameters and extremes. Inspiration for this project came from KNMI's production of a 35-year wind atlas for the North Sea (KNWatlas;1997-2013Stepek et al, 2015) which was also produced using the ALADIN-HIRLAM system.…”

Section: Introductionmentioning

confidence: 99%

Met Éireann high resolution reanalysis for Ireland

Gleeson¹,

Whelan²,

Hanley³

2017

Adv. Sci. Res.

View full text Add to dashboard Cite

Abstract. The Irish Meteorological Service, Met Éireann, has carried out a 35-year very high resolution (2.5 km horizontal grid) regional climate reanalysis for Ireland using the ALADIN-HIRLAM numerical weather prediction system. This article provides an overview of the reanalysis, called MÉRA, as well as a preliminary analysis of surface parameters including screen level temperature, 10 m wind speeds, mean sea-level pressure (MSLP), soil temperatures, soil moisture and 24 h rainfall accumulations. The quality of the 3-D variational data assimilation used in the reanalysis is also assessed. Preliminary analysis shows that it takes almost 12 months to spin up the deep soil in terms of moisture, justifying the choice of running year-long spin up periods. Overall, the model performed consistently over the time period. Small biases were found in screen-level temperatures (less than −0.5 • C), MSLP (within 0.5 hPa) and 10 m wind speed (up to 0.5 m s −1 ) Soil temperatures are well represented by the model. 24 h accumulations of precipitation generally exhibit a small positive bias of ∼ 1 mm per day and negative biases over mountains due to a mismatch between the model orography and the geography of the region. MÉRA outperforms the ERA-Interim reanalysis, particularly in terms of standard deviations in screen-level temperatures and surface winds. This dataset is the first of its kind for Ireland that will be made publically available during spring 2017.

show abstract

“…Higher resolution atmospheric reanalyses for the Baltic region have also been made. The BaltAn65' reanalysis (Luhamaa et al, 2011) has 11 km grid resolution and covers the Baltic Sea, but only the eastern part of the North Sea. The recent project Euro4M has resulted in a data set of 22 km grid resolution and covers a larger domain for the time period 1979Á2013 (Dahlgren et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 19892013

Axell¹,

Liu

2016

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

A B S T R A C T A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data.

show abstract

High resolution re-analysis for the Baltic Sea region during 1965–2005 period

Cited by 34 publications

References 19 publications

MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

Met Éireann high resolution reanalysis for Ireland

Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 19892013

Contact Info

Product

Resources

About