Center for Weather Forecast and Climate Studies Brazilian National Institute for Space Researches Assessing observation impacts on the INPE/CPTEC global.

Apresentação em tema: "Center for Weather Forecast and Climate Studies Brazilian National Institute for Space Researches Assessing observation impacts on the INPE/CPTEC global."— Transcrição da apresentação:

1 Center for Weather Forecast and Climate Studies Brazilian National Institute for Space Researches Assessing observation impacts on the INPE/CPTEC global data assimilation system over South America Assessing observation impacts on the INPE/CPTEC global data assimilation system over South America SIXTH WMO WORKSHOP ON THE IMPACT OF VARIOUS OBSERVING SYSTEMS ON NWP Shanghai, China 10-13 May 2016by LUIZ SAPUCCI LUIZ SAPUCCI Data Assimilation Group Modeling and Development Division luiz.sapucci@cptec.inpe.br

2 Motivation CPTEC/INPE’s mission: "To provide to country with weather and climate predictions, for the benefit of the Brazilian people.“ The proposal of this center is to make global modeling to offer the best forecast over Brazilian territory and neighboring region; The focus of this presentation is the observation impact in the improvement of the forecast quality over South America. The results from several studies recently developed (or in development) at CPTEC are showed here and the respective collaborators are mentioned.

3 NWP’s challenger over South America Figure from BRAMS website Amazonian forest Larger portion in the tropical region; Located between the two largest oceans: larger regions with poor network of observation at surface. Specific adjustments in the model are necessary, consequently the observation impact presents some particularity, for example larger dependency of satellite observations, which are explored in this presentation.

4 Content of the presentation Model used: –CPTEC global model T213L42 (42km) Data assimilation systems available: –LETKF (Local Ensemble Transform Kalman Filter); Research mode; –GSI (Gridpoint Statistical Interpolation); Operational mode; Studies about impact of observation at CPTEC: –FSO using LETKF at CPTEC/INPE (state-space); –FSO using GSI at CPTEC/INPE (observation-space); –Impact of Land Data Assimilation at CPTEC model on precipitation; –Impact of RO-GNSS refractivity data using LETKF/CPTEC; –Impact of Radar Data Assimilation on precipitation. FSO using LETKF at CPTEC/INPE (state-space);

5 FSO using LETKF (state-space) In Liu and Kalnay [2008, see also Li et al (2010)] is introduced an ensemble-based approach to assess the impact of observations on the forecasts formulated using a state-space aspect where C k is a positive semidefinite suitable weighting matrix, and the forecast error is calculated for a forecast started at time t l < t k. Considering this measure the ensemble-based forecast error reduction can be written as When m=5 represents the case with verification at 24-h forecast (assuming 6-h assimilation cycle) J. Liu, E. Kalnay: Estimating observation impact without adjoint model in an ensemble Kalman filter. 2008. In QJRMS, 134, 1327-1335. H. Li, J. Liu, E. Kalnay: Correction of ‘Estimating observation impact without adjoint model in an ensemble Kalman filter’. 2010. In QJRMS, 136, 1652-1654. Figure: Schematic representation of time line and the relevant forecast error definitions. Fabio. Preciso saber o que é os termos das equações Quem é L? o v de residuo? Fabio, a matriz Ck que é escrita da forma? Fabio, esta certo a mudança? Retirei as outras para simplificar Sapucci: essa notação é chata mesmo... -“K” e “L” são os tempos de validade e inicio (e.g., K|L significa valida para K e inicio em L); esse “L” é definido apenas para dizer que estamos verificando uma previsão futura (forward) pois L<K. -O superescrito “V” é de verificação e é utilizado aqui para identificar o estado de verificação x V (Obs.: no GSI estamos usando as observações ao invés desse estado). Sapucci: não entendi a pergunta com relação a matriz Ck. Essa matriz define uma norma. Aqui nós utilizamos uma norma de energia total úmida. No GSI nós usamos a matriz R -1 que foi utilizada na própria geração da análise. Sapucci: isso é possível, mas não está certo de acordo com o que fizemos. Nós fizemos o mesmo que a abordagem de adjuntos fazia, que era verificar o impacto das observações nas previsões de 24-h. Para isso era feita a diferença dos erros entre as previsões de 30-h e as previsões de 24-h. (Obs.: é apresentada uma formulação genérica com relação ao tempo de verificação e no final escolhemos m=1, o que implica que é verificado em 6-h, ou seja, análise). Sapucci, a formulação que estava aqui é a que mais se parece com a publicada nos artigos citados. Para não ficar mais confuso eu reescrevi a formulação usando a notação de acordo com a que vamos utilizar no GSI, que já estava pronta nos slides.

6 Figure: Bar plots of fractional observation impact (top left), observation count (top right), impact per observation (bottom left) and positive impact (bottom right) on 24-h forecasts (m=5) during February 2004. FSO using LETKF at CPTEC/INPE (state-space) Global observation usage summary Fabio, que dados são esses Fabio, completa a referencia com o nome do artigo e a revista que pretende Sapucci: foi falha minha... VADWND: é vento (u,v) obtido por meio de dados de radar SPSSMI: é vento (u,v) em superfície oceânica (semelhante ao QuikSCAT) SYNDAT: é dado sintético gerado no NCEP Diniz, F. L. R. et al. 2016: An Observation Impact Tool for CPTEC LETKF. Weather and Forecasting (AMS). In preparation.

7 Figure: Bar plots of fractional observation impact (top left), observation count (top right), impact per observation (bottom left) and positive impact (bottom right) on 24-h forecasts (m=5) during February 2004. FSO using LETKF at CPTEC/INPE (state-space) Global observation usage summary Fabio, completa a referencia com o nome do artigo e a revista que pretende Ok! Diniz, F. L. R. et al. 2016: An Observation Impact Tool for CPTEC LETKF. Weather and Forecasting (AMS). In preparation.

8 Content of the presentation Model used: –CPTEC global model T213L42 (42km) Data assimilation systems available: –LETKF (Local Ensemble Transform Kalman Filter); Research mode; –GSI (Gridpoint Statistical Interpolation); Operational mode; Studies about impact of observation at CPTEC: –FSO using LETKF at CPTEC/INPE (state-space); –FSO using GSI at CPTEC/INPE (observation-space); –Impact of Land Data Assimilation at CPTEC model on precipitation; –Impact of RO-GNSS refractivity data using LETKF/CPTEC; –Impact of Radar Data Assimilation on precipitation.

9 FSO using GSI (observation-space) In Todling (2013) is introduced an observation-space approach to assess the impact of observations on the forecasts formulated using a residual Where C k is a positive semidefinite suitable weighting matrix, and the forecast error is calculated for a forecast started at time t l < t k. Considering this measure the observation-space forecast error reduction can be written as When m=1 represents the case with verification at analysis time and choosing the particularly convenient norm C k =R k -1, results Todling, R. Comparing two approaches for assessing observation impact. 2013. In Monthly Weather Review, 141, 1484-1505. Sapucci, é importante notar que é apenas uma relação de OmF & OmA, simplificando, e muito, a formulação tradicional de FSO. Essas medidas estão disponíveis em qualquer sistema de assimilação de dados. Sapucci, no LETKF nós utilizamos uma norma de energia total úmida para comparar resultados de vento com temperatura, com pressão a superfície, etc... O que nos dava uma medida de energia. Aqui já estamos utilizando a própria matriz R -1, o que implica que a medida seja adimensional pois estamos dividindo uma medida quadrática pela covariância do erro da observação.

10 Figure: Bar plots of fractional observation impact (left), observation count (right), on all analysis for control experiment (blue) and for the experiment adding mesonet data over Brazil (red) during January 2013. Global observation usage summary Fabio, completa a referencia com o nome do artigo e a revista que pretende FSO using GSI at CPTEC/INPE (observation-space) Ok! Diniz, F. L. R. et al. 2016: Assessing observation impacts using CPTEC Global GSI. Meteorological Applications (RMetS). In preparation.

11 Figure: Bar plots of impact per observation (left) and positive impact percentage (right) on all analysis for control experiment (blue) and for the experiment adding mesonet data over Brazil (red) during January 2013. FSO using GSI at CPTEC/INPE (observation-space) Global observation usage summary Diniz, F. L. R. et al. 2016: Assessing observation impacts using CPTEC Global GSI. Meteorological Applications (RMetS). In preparation.

12 Figure: Bar plots of fractional observation impact (left), observation count (right), on all analysis for control experiment (blue) and for the experiment adding mesonet data over Brazil (pink) during January 2013. Global observation usage summary FSO using GSI at CPTEC/INPE (observation-space); Diniz, F. L. R. et al. 2016: Assessing observation impacts using CPTEC Global GSI. Meteorological Applications (RMetS). In preparation. This access method to observation impact is relatively simple and can be used in operational mode, which will be implemented at CPTEC for monitoring of the assimilated data base. There is the indication that in this region more dense surface station network associated with satellite data can be useful for reduce the radiosonde data dependence.

13 Content of the presentation Model used: –CPTEC global model T213L42 (42km) Data assimilation systems available: –LETKF (Local Ensemble Transform Kalman Filter); Research mode; –GSI (Gridpoint Statistical Interpolation); Operational mode; Studies about Impact of observation: –FSO using LETKF at CPTEC/INPE (state-space); –FSO using GSI at CPTEC/INPE (observation-space); –Impact of Land Data Assimilation at CPTEC model on precipitation; –Impact of RO-GNSS refractivity data using LETKF/CPTEC; –Impact of Radar Data Assimilation on precipitation.

14 Soil Moisture Increment Diference of Latent Heat flux (Qle) (OpenLoop – LDAS) Land Data Assimilation at CPTEC-AGCM Screen-Level Data assimilation method: OI Period of study: 1998 to 2014. de Mattos, J G Z et al. 2016: A screen-level Data Assimilation at CPTEC AGCM, Journal of Hydrometeorology, in submission

15 Bias removed Pearson correlation without data assimilation = 0.44 with data assimilation = 0.80 Global Medium → (OpenLoop - CMAP) fix annual cycle Impact land data assimilation on simulated precipitation Mattos, J G Z et al. 2016: A screen-level Data Assimilation at CPTEC AGCM, Journal of Hydrometeorology, in submission The figure above shows the average difference of the precipitation over complete period (1998 to 2014)

16 Content of the presentation Model used: –CPTEC global model T213L42 (42km) Data assimilation systems available: –LETKF (Local Ensemble Transform Kalman Filter); Research mode; –GSI (Gridpoint Statistical Interpolation); Operational mode; Studies about Impact of observation: –FSO using LETKF at CPTEC/INPE (state-space); –FSO using GSI at CPTEC/INPE (observation-space); –Impact of Land Data Assimilation at CPTEC model on precipitation; –Impact of RO-GNSS refractivity data using LETKF/CPTEC; –Impact of Radar Data Assimilation on precipitation.

17 Impact of RO-GNSS refractivity data at LETKF CPTEC using ROPP A specific operator of the RO-GNSS refractivity data into the Local Ensemble Transform Kalman Filter (LETKF) system is being developed applying the Radio Occultation Processing Package (ROPP) from GRAS-SAF/Eumetsat (GNSS Receiver for Atmospheric Sounding- Satellite Applications Facilities/European Organization for the Exploration of Meteorological Satellites) Schematic figure showing the LETKF assimilation cycle, in which involve CPTEC global model, observations operator and data pre-processing. Flow chart of the operator of the RO-GNSS observations coupled in the LETKF system calling the ROPP modules in parallel processing.

18 Experiment to evaluate the impact: Running with and without refractivity data; Period September/2011.

19 Anomaly correlation in geopotential hight at 500 Hpa Spatial field of gain percentage (green) and losses (red) of the RMSE with data assimilation of GNSS-RO.

20 Score card for RMSE gain percentage 20/21 Sapucci, L. F. et al. (2016): Inclusion of GNSS-RO data into CPTEC LETKF using the ROPP as an observation operator. Met. Apps, 23: 328–338. doi: 10.1002/met.1559

21 Outline Model used: –CPTEC global model T213L42 (42km) Data assimilation systems available: –LETKF (Local Ensemble Transform Kalman Filter); Research mode; –GSI (Gridpoint Statistical Interpolation); Operational mode; Studies about Impact of observation: –FSO using LETKF at CPTEC/INPE (state-space); –FSO using GSI at CPTEC/INPE (observation-space); –Impact of Land Data Assimilation at CPTEC model on precipitation; –Impact of RO-GNSS refractivity data using LETKF/CPTEC; –Impact of Radar Data Assimilation on precipitation.

22 Radar Data Assimilation d03 Sao Roque Radar (Sao Paulo) Pico do Couto Radar (Rio de Janeiro) S-band Doppler Radar Observations: Refletictivity Radial Velocity Radar Data Assimilation using the WRF Data Assimilation System (WRFDA). Radar data assimilation will cycle hourly and restart each 6 hour using initial condition from domain d02 (from regional cycle)

23 Impact of radar data assimilation Radar Data = huge amount of data. 3D-Var + Radar Data = lack of proper balance in the final analysis. Vendrasco, E. P. et al. (2016): Constraining a 3DVAR Radar Data Assimilation System to Improve Short-Range Precipitation Forecasts. JAMC. DOI: http://dx.doi.org/10.1175/JAMC-D-15-0010.1http://dx.doi.org/10.1175/JAMC-D-15-0010.1 Was added a constraining to Control Noise in High-Resolution and obtain Analysis with Multi-scale Balance

24 Center for Weather Forecast and Climate Studies Brazilian National Institute for Space Researches Thank you for your attention. Assessing observation impacts on the INPE/CPTEC global data assimilation system over South America Assessing observation impacts on the INPE/CPTEC global data assimilation system over South America