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JRA Activities

JRA Activities.

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JRA Activities

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  1. JRA Activities Yayoi Harada*1 on behalf of Masashi Ujiie*2, Hitoshi Yonehara *2, RyojiNagasawa*2,Takayuki Tokuhiro*2, Akira Shimokobe*2, Hitoshi Sato*2, Kei Saitou*2, ShokichiYabu*2, Daisuke Hotta*1, Kazutaka Yamada*2, RyoheiSekiguchi*3, TakafumiKanehama*3, Hiromi Owada *2, Masayuki Nakagawa*1, and Shinya Kobayashi *3 *1 Meteorological Research Institute / Japan Meteorological Agency *2 Numerical Prediction Division / Japan Meteorological Agency *3 Climate Prediction Division / Japan Meteorological Agency S-RIP – SPARC DA Workshop joint session at ECMWF in Reading, UK25 October 2017

  2. Outlines • Recent improvements of the JMA’s operational global NWP system • Overview of JMA’s Global Spectral Model (GSM) • Recent Major Upgrades of JMA’s GSM • Implementation of a non-orographic gravity wave parameterization scheme • Implementation of a methane oxidation scheme • Improvements in the convection scheme • Recent improvement of GNSS-RO data assimilation • Next Japanese reanalysis: JRA-3Q

  3. Overview of JMA’s GSM (dynamical core) For more detail, please see “OUTLINE OF THE OPERATIONAL NUMERICAL WEATHER PREDICTION AT THE JAPAN METEOROLOGICAL AGENCY” http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/index.htm

  4. Overview of JMA’S GSM (parameterization schemes)

  5. Recent major upgrades of JMA’s GSM(after JRA-55, 1/2) parameterization resolution

  6. Recent major upgrades of JMA’s GSM(after JRA-55, 2/2) parameterization resolution • After JRA-55, almost all of parameterizations in GSM such as convection, cloud, radiation, boundary layer, sea & land surface, sea ice treatment and subgrid gravity wave schemes have been improved in more physically consistent ways. • .

  7. Improvements of parameterization schemes • Introduction of a non-orographic gravity wave parameterization scheme (GSM1403) • Introduction of a methane oxidation scheme (GSM1705) • Improvements in the convection scheme (GSM1603, GSM1705)

  8. Time-Height Cross of zonal mean zonal wind in AMIP-type run using low resolution version of GSM GSM1705with NGW • QBO-like oscillation • small amplitudeand short period • Weak downward propagation in the lower stratosphere • AMIP run, TL159L100 • Period: 1981-1990 GSM1705 without NGW (Rayleigh Friction) • No QBO-like oscillation JRA-55

  9. Improvements of parameterization schemes • Introduction of a non-orographic gravity wave parameterization scheme (GSM1403) • Introduction of a methane oxidation scheme (GSM1705) • Improvements in the convection scheme (GSM1603, GSM1705)

  10. Outline of parameterization (Untch and Simmons, 1999) • Considering processes • (1) generation of water vapor due to oxidation of methane • (2) extinction of water vapor due to photodissociation in the mesosphere • Tendency of specific humidity due to chemical processes is parameterized as follows: • assume that the volume mixing ratio of H2O and CH4 are balanced • coefficients k1 and k2 are function of pressure only (1) (2) vertical profile of coefficients k1 and k2 red: k1 , blue: k2 , green: k1+k2

  11. Model climatology of specific humidity(Jan.) [hPa] w/o chemical process w/ chemical process Reference(SPARC) • Compared with the SPARC climatology (Randel et al., 2004), specific humidity was too low without chemical processes. • Model climatology of specific humidity in the middle atmosphere was improved with chemical processes. 0.01 0.1 1 10 100 [10-6kg/kg] EQ 90S 90N

  12. Improvements of parameterization schemes • Introduction of a non-orographic gravity wave parameterization scheme (GSM1403) • Introduction of a methane oxidation scheme (GSM1705) • Improvements in the convection scheme (GSM1603, GSM1705)

  13. Problems in the GSM’s convection scheme (before GSM1603) • Lack of the important terms in the formulation of the convection scheme in GSM • Precipitation generation from convective cloud condensate before reaching cloud top (requires an iterative calculation in the GSM’s source code framework). • More ice cloud, snowfall and latent heat release in convective updraughtwhich results in top heavy convective heating profile. • Convective snow melting • the snow melting process was not explicitly calculated, implicitly treated as a form of artificial energy correction, to avoid the noises due to the sharp cooling around freezing level. Lin et al. (2012) Important to consider these terms although the computational cost is high. 13

  14. Generated precipitation fluxes (convection) With the conversion from cloud to precip. w/o the conversion from cloud to precip. Colors: Generated precipitation fluxes(mm/6hr) Contours: Dissipated (e.g. evaporation) precipitation fluxes (mm/6hr) Convective precipitation rates (mm/6hr) *Surface precipitation looks similar, but the precipitation was generated different levels. *More ice, to be converted to precipitation in the upper troposphere, makes convection more buoyant and creates larger convective heating rate there. *Importance of three dimensional structure of precipitation.

  15. Improvement in the convective snow melting(GSM1403 to GSM1603) In GSM1603, the snow melting was explicitly considered, rather than the artificial energy correction used in GSM1403 and before. frequency of convective cloud top Initialized at 00UTC 15 Aug. 2012, T+24 (Johnson et al. 1999) GSM1403 GSM1603 ・GSM1603 clearly represents the cumulus congestus around the freezing level.

  16. Improvement in the convective snow melting(GSM1603 to GSM1705) In GSM1705, the formulation of convective snow melting was revised to represent sharper melting layer than that in GSM1603 frequency of convective cloud top Initialized at 12UTC 11 May 2017, T+24 GSM1705 GSM1603 (Johnson et al. 1999) ・GSM1705 represents the tri-modal structure of the tropical convection better than the older versions.

  17. RECENT IMPROVEMENTs of GNSS Radio OCCULTATIOn assimilation

  18. Replacement to bending angle data assimilation in the global NWP system • Refractivity data assimilation (~ 17 Mar. 2014) • Vertical thinning : 500m • Upper limit of data usage : 30km • Code of observation operator: original • Observational error: defined independently in five latitudinal bands (60–90N, 20–60N, 20S–20N, 60–20S, 90–60S) as a function of height for each satellite based on statistics of O-B • Bending angle data assimilation (18 Mar. 2014 ~, same date as the launch of GSM1403) • No vertical thinning • Upper limit of data usage: 60km • Code of observation operator: 1D operator in ROPP • Observational error: depends on impact height “h” only • 1% of observed bending angle for h > 10 km • linear variation from 20% at h=0 km to 1% at h=10 km • Lower limit of 3 micro rad.

  19. Background fit to radiosonde temperature Mean error (left) and root mean square error (right) of background (6 hour forecast) fit to radiosonde temperature measurements in the Northern Hemisphere in August 2013.

  20. Forecast fit to radiosonde temperatureRMSE of temperature forecast at 20 hPa in the Tropics K K

  21. Summary (the JMA operational global NWP system) • JMA upgraded the operational Global Spectral Model several times in the recent 5 years • Almost all of parameterization schemes were upgraded, particularly convection and cloud, and non-orographic gravity waves and methane oxidation schemes are newly implemented. As a result, the following improvements are found: • Representation of QBO-like oscillation in the tropical stratosphere • Representation of stratospheric water vapor comparable to SPARC climatology • Reasonable vertical profile of convective heating • Representation of cumulus congestus in the tropics • Implementation of bending angle data assimilation improve the stratospheric analysis and forecasts.

  22. The next Japanese reanalysis • JRA-3Q (pronounced as “Thank you!” in Japanese) • Japanese Reanalysis for Three Quarters of a Century • Provisional specifications(as of October 2017) • Higher resolution: TL319L60 -> TL479L100 • 40 km in horizontal, 100 layers up to 0.01 hPain vertical • 4D-Var with the TL319 inner resolution • Extending the reanalysis period back in time • Atmospheric reanalysis from 1947 to present • New observations • Observations newly rescued and digitised by ERA-CLIM et al. • Improved satellite observations through reprocessing • Bending Angle from GNSS RO is newly implemented (up to 60 km) • JMA’s own tropical cyclone bogus • Aiming at starting production by the end of FY2018 • Product will be available during FY2022. • JRA-55 near-real time production is planned to be continued until the end of FY2022.

  23. Thank you for attention !

  24. References • Arakawa, A. and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I., J.Atmos.Sci., 31, 674-701. • Beljaars, A. C. M. and A. A. M. Holtslag, 1991: Flux parameterization over land surfaces for atmosphericmodels., J.Appl.Meteor., 030, 327-341. • Furukawa, T. and A. Shimokobe, 2013: Operational Implementation of Modification to Stratocumulus Parameterization Scheme in JMA's Global Spectral Model., CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 43, 6.5-6.6. • Han, J. and H. Pan, 2011: Revision of Convection and Vertical Diffusion Schemes in the NCEP Global Forecast System., Wea. Forecasting, 26,520-533. • Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, W. H. Schubert, 1999: Tri-modal Characteristics of Tropical Convection., J. Climate., 12, 2397-2418. • Lin, Y., L. J. Donner, J. Petch, P. Bechtold, J. Boyle, S. A. Klein, T. Komori, K. Wapler, M. Willett,X. Xie, M. Zhao, S. Xie, S. A. McFarlane and C. Schumacher., 2012: TWP-ICE global atmosphericmodelintercomparison: Convection responsiveness and resolution impact., J. Geophys. Res., 117, D09111. • McLandress, C. and Scinocca, J. F., 2005: The GCM response to current parameterizations of nonorographic gravity wave drag., J. Atmos. Sci, 62(7), 2394-2413.

  25. References • Orr, A., Bechtold, P., Scinocca, J., Ern, M. and Janiskova, M., 2010: Improved middle atmosphere climate and forecasts in the ECMWF model through a nonorographic gravity wave drag parameterization., J. Clim., 23(22), 5905-5926. • Randel, W., P. Udelhofen, E. Fleming, M. Geller, M. Gelman, K. Hamilton, D. Karoly, D. Ortland, S. Pawson, R. Swinbank, F. Wu, M. Baldwin, M.-L. Chanin, P. Keckhut, K. Labitzke, E. Remsberg, A. Simmons, and D. Wu, 2004: The SPARC Intercomparison of Middle-Atmosphere Climatologies. J. Climate, 17, 986–1003. • Scinocca, J. F., 2003: An Accurate Spectral Non-orographic Gravity Wave Drag Parameterization for General Circulation Models., J. Atmos. Sci., 60, 667-682. • Simmons, A. J. and D. M. Burridge, 1981: An energy and angular-momentum conserving vertical finite difference scheme and hybrid vertical coordinates., Mon.Wea.Rev., 109, 758-766. • Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model., Quart.J.Roy.Meteor.Soc., 116, 435-460. • Untch, A. and Simmons, A. J. 1999: Increased stratospheric resolution in the ECMWF forecasting system, ECMWF Newsletter 82, ECMWF, Reading, UK.

  26. References • Yonehara, H., M. Ujiie, T. Kanehama, R. Sekiguchi and Y. Hayashi, 2014: Upgrade of JMA’s Operational NWP Global Model, CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 44, 6.19-6.20. • Yonehara, H., T. Tokuhiro, R. Nagasawa, M. Ujiie, A. Shimokobe, M. Nakagawa, R. Sekiguchi, T. Kanehama and H., K. Saitou, 2017: Upgrade of parameterization schemes in JMA’s operational global NWP model., CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 47, 4.17-4.18.

  27. BACKUP

  28. Update of parameterization schemes (18 Mar. 2014) • Revising a stable boundary layer scheme →Improving wind fields and diurnal temperature variation in stable conditions • Revising albedo parameters in the desert areas →Reducing clear sky radiation biases • Introducing two-stream approximation for long wave radiation scheme →Accelerating radiation code and improving the middle atmosphere temperature structure • Introducing a non-orographic gravity wave forcing scheme →Improving the middle atmosphere climate and representation of long-term oscillation in the tropical lower stratosphere such as QBO • Changing the application criteria of energy correction terms in convective parameterization →Improving general circulation and global precipitation distribution • Applying 2nd-order linear horizontal diffusion in the divergence equation and adjusting 4th-order linear diffusion as a sponge layer around the model top region →Improving the middle atmosphere forecast accuracy

  29. Update of cumulus convection and cloud scheme(24 Mar. 2016) • Cumulus convection (Arakawa-Schubert) • Revising budget equation of moist static energy → Improving energy conservation • Revising estimation of static energy at cloud base • Entrainment rate based on Jakoband Siebesma(2003), adding static energy perturbation at cloud base → Improving convective heating profile • Revising snow melting process → Improving convective heating profile • Introducing fallout of precipitation between cloud base and cloud top • Iterative calculation to estimate entrainment rate → Improving convective heating profile • Cloud (PDF-based parameterization (Smith 1990)) • Removing increase of PDF width by cumulus effect → Reducing dry bias in the middle troposphere • Revising cloud ice falling process → Reducing time step dependency

  30. Update of cumulus convection and cloud scheme(25 May. 2017) • Cumulus convection (Arakawa-Schubert) • Revising snow melting and precipitation evaporation → Improving heating profiles, representation of tri-modal structure of cumulus (Johnson et al. 1999) and computational stability • Revising calculation of precipitation fluxes → Improving convective heating profile • Cloud (PDF-based parameterization (Smith 1990)) • Revising snow melting and precipitation evaporation → Improving representation of synoptic scale fronts, surface temperature forecasts and computational stability • Revising the coalescence process in precipitation generation → Improving representation of precipitation • Cloud radiation • Considering convective cloud towers (“stems” of convection) in the cloud radiation scheme, rather than considering in the cloud scheme → Improving radiation budget and convective activity over the land • Refinement of optically effective radius for liquid cloud → Improving radiation budget

  31. Roles of GSM as the operational model • GSM provides: • basic information for short- and medium-range, one week, one month and seasonal forecasts • basic information for typhoon track and intensity forecasts • first guess of the operational global data assimilation system • Older versions of GSM are used in the JRA-25 and JRA-55 reanalyses • lateral and upper boundary conditions for the operational Meso-Scale Model • etc

  32. GSM1403 -1.67 sensible and latent heat fluxes total longwave shortwave -0.844 GSM1603 Energy budget -0.857 GSM1705

  33. Data assimilation and forecast experiments (low res. ver.) assimilation : TL479 (outer) / TL159 (inner) forecast : TL479 period : 2015 Aug. and 2016 Jan. AN and FG departure difference between the experiments against radiosonde zonal wind obs. AN Departure FG Departure 2015 12/21 – 2016 2/11 T+5D zonal mean error of T [K] against analysis for August 2015. without NGW GSM1705 2015 7/21 – 2015 9/11 large departure small departure Reduction of AN and FG departure in the Tropical lower stratosphere (AN Departure = analysis – obs. and FG Departure = first guess – obs.) Alleviation of cold pole bias in GSM1705 (with NGW parameterization), though warm bias in GSM1705 associated with weaker polar jet

  34. PROBLEMS in convection-dependent PDF width of the large scale cloud

  35. Cloud scheme in GSM PDF Water vapour • PDF scheme based on Smith 1990 • Total water is divided into water vapour and cloud water using PDF • A top hat function is employed as PDF Width σs Cloud water • The convection scheme does not count the cloud amount and water in convection explicitly because of the assumption that the convection area is too small and negligible. • To count the cloud amount and water, the PDF width was stretched to increase the amount of water vapor. • Historically, the treatment was introduced to modify the low bias of mid level cloud in the tropics. • The treatment reproduces too much cloud water which results in too much precipitationand dry atmosphere. • “side effects” of this treatment overweighed its intended benefit above. Total water qT Mean total water qT Saturated relative humidity q*

  36. Bias of specific humidity • Reducing the effect of convection in PDF width shows significant improvement of dry bias in midlevel in the tropics, because… • Too much conversion from water vapour to precipitation via cloud water was reduced. • More activated convection brought more mid level humidity by detrainment. Q700 New Old Verification against radiosonde; mean error at T+72, from July to September in 2015

  37. Introduction of parameterization of chemical processes • Main processes to determine the distribution of stratospheric water vapor • saturation in the tropical tropopause layer, meridional circulation, chemical processes, … • In GSM, these processes were not well represented. • In particular, chemical processes were not quite considered. • Methane oxidation parameterization scheme based on Untch and Simmons(1999) was implemented in GSM1705. • Due to introduction of this scheme, representation of model climatology of stratospheric water vapor was improved.

  38. OBSERVATIONS assimilated in the GLOBAL ANALYSIS

  39. Observations assimilated in the JMA global analysis (as of September 2017)

  40. Assimilation Status of Satellite Data (Global Analysis) CSR: Clear Sky Radiance on water vapor channels, AMV: Atmospheric Motion Vector, OSWV: Ocean Surface Wind Vectors (as of September 2017)

  41. History on the use of RO data at JMA Global Analysis Current status of provided data Period of operational use Satellite (Available for experimental use) CHAMP 22 Mar. 2007 ~ 20 Nov. 2007 C/NOFS 18 Dec. 2012 ~ 06 Nov. 2013 No dissemination SAC-C (20 Dec. 2010 ~ 2 Aug. 2011) 30 Nov. 2009 ~ 04 Dec. 2009 GRACE-A 18 Dec. 2012 ~ 30 Nov. 2009 ~ Metop-A Operational use Refractivity: ~ 17 Mar. 2014 Bending angle: 18 Mar. 2014 ~ Metop-B 28 Nov. 2013 ~ COSMIC 01 Nov. 2010 ~ TerraSAR-X 18 Dec. 2012 ~ GRACE-B 15 Dec. 2016 ~ TanDEM-X (01 Jul. 2014 ~) Validating for the operational use KOMPSAT-5 (17 Feb. 2017 ~) FY-3C (23 Aug. 2017? ~) As of September 2017

  42. Observing system experiments to identify the impact of RO data assimilation • Periods of the cycle experiments • August 2013: 10 July – 11 September,2013 • January 2014: 10 December,2013 – 11 February,2014 • REFRAC • Refractivity data assimilation • Settings for assimilating RO data corresponds to the previous operational ones (vertical thinning, upper limit of data usage and observational error) • BANGLE • Bending angle data assimilation • Settings for assimilating RO data corresponds to the current operational ones • NO RO • Without RO data assimilation

  43. Summary on the experiments • Both BANGLE and REFRAC reduced large biases in the background (6 hour forecast) relative to radiosonde observations which appeared in the upper troposphere and stratosphere in case of NO RO • The improvement of the stratospheric temperature forecasts in BANGLE was larger than in REFRAC. This is because the background of BANGLE was improved by assimilating RO profiles above 30 km

  44. Data assimilation system

  45. Forecast model

  46. Challenges of future reanalyses regarding satellite data • How to improve time-consistency of reanalysis while using changing observing systems? • Availability of “anchoring observations” that constrain model biases is a key. • Data rescue of early meteorological satellites • Homogenisation through recalibration and reprocessing • More effective use of satellite data through improvements in observation operators such as RT models • Rain-affected radiances • Tropospheric channel radiances over land • Better characterization of spectral response functions (SRFs)

  47. Data rescue: Early meteorological satellites Current availability of satellite radiances 1st stratospheric sounder SSU on TIROS-N 1st MW humidity sounder AMSU-B on NOAA16 1st IR sounder VTPR on NOAA2 1st MW sounder MSU on TIROS-N 1st MW imager SSM/I on DMSP8

  48. Data rescue: Early meteorological satellites Possible extension through data rescue by ERA-CLIM, CM SAF and elsewhere Stratospheric sounders SCR & PMR on Nimbus MW humidity sounder SSM/T-2 on DMSP Hyperspectral IR sounders IRIS & SIRS on Nimbus MW sounders NEMS & SCAMS on Nimbus MW imager SMMR on Nimbus 7

  49. JMA’s contribution to SCOPE-CM and reanalysis SCOPE-CM: Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring The reprocessed AMVs and CSRs are provided to the Japanese 55-year Reanalysis (JRA-55) GMS/MTSAT reprocessing at JMA/MSC GMS GMS-3 GMS-4 GMS-5 GOES-9 MTSAT-1R AMV (IR) AMV (VIS) AMV (WV) CSR (IRs; 10.8, 12.0μm) CSR (WV; 6.8μm) CSR (IR; 3.8μm) Jan Nov 1979 Mar 1987 Dec 1989 Jul 1995 Jun 2003 Jul 2005 Sep 2009

  50. The number of AMVs assimilated in JRA-55 Meteosat 0 degree GOES West reprocessed Indian Ocean GOES East GMS/MTSAT • “New” reprocessed data will be available for future reanalyses. • CIMSS has recently reprocessed GOES AMVs from 1995 to mid 2013. • Reprocessing of AMV/CSR/ASR is underway in the framework of SCOPE-CM phase 2 project (2014-2018) reprocessed

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