Skip to main content
SpringerLink
Log in

Performance of 4D-Var Data Assimilation on Extreme Snowfall Forecasts over the Western Himalaya Using WRF Model

  • Original Article
  • Published:
Asia-Pacific Journal of Atmospheric Sciences Aims and scope Submit manuscript

Abstract

The accurate predictions of extreme precipitation/snowfall events are very helpful in identifying the severe avalanche/landslide prone hazard areas in advance over high mountainous regions. The Weather Research and Forecasting model (WRF) version 3.9 has been used to investigate the performance of Four-Dimensional Variational data assimilation (4D-Var) on Three-Dimensional Variational data assimilation (3D-Var) by considering two extreme snowfall events (23–26 January 2017 and 05–08 February 2019) over the Western Himalaya (WH). The result shows that the 4D-Var performed better than the 3D-Var for both the events by analyzing domain-averaged error and sensitivity parameter analysis. The initial state model variable’s domain-averaged error analysis revealed that 4D-Var has great potential to improve the initial conditions than the 3D-Var from lower to the upper atmosphere. Sensitivity parameter analysis also supports 4D-Var has more sensitive than the 3D-var especially in the lower and upper atmosphere by changing temperature and moisture fields along with winds circulations. From statistical skill scores analysis, 4D-Var performed well to reproduce the extreme snowfall events than the 3D-Var over WH.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Andersson, E., Hollingsworth, A., Kelly, G., Lönnberg, P., Pailleux, J., Zhang, Z.: Global observing system experiments on operational statistical retrievals of satellite sounding data. Mon. Weather Rev. 119, 1851–1864 (1991)

    Article  Google Scholar 

  • Barker, D.M., Huang, W., Guo, Y.R., Bourgeois, A.J., Xiao, Q.N.: A three-dimensional variational data assimilation system for MM5: Implementation and initial results.Mon. Weather Rev. 132(4), 897–914 (2004)

    Article  Google Scholar 

  • Bonekamp, P.N.J., Collier, E., Immerzeel, W.W.: The impact of spatial resolution, land use, and spinup time on resolving spatial precipitation patterns in the Himalayas. J. Hydrometeorol. 19, 1565–1581 (2018)

    Article  Google Scholar 

  • Bookhagen, B., Burbank, D.W.: Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res. Earth Surf. 115, F03019 (2010)

    Article  Google Scholar 

  • Chevuturi, A., Dimri, A.P.: Investigation of Uttarakhand (India) disaster-2013 using weather research and forecasting model. Nat. Hazards. 82(3), 1703–1726 (2016)

    Article  Google Scholar 

  • Chen, F., Dudhia, J.: Coupling an advanced land surface hydrology model with the Penn State-NCAR MM5 modelling system, Part I: model implementation and sensitivity. Mon Weather Rev. 129, 569–585 (2001)

    Article  Google Scholar 

  • Collier, E., Immerzeel, W.W.: High-resolution modeling of atmospheric dynamics in the Nepalese Himalaya. J. Geophys. Res. Atmos. 120, 9882–9989 (2015)

    Article  Google Scholar 

  • Copernicus Climate Change Service (C3S).: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS, 2017). https://cds.climate.copernicus.eu/cdsapp#!/home

  • Daley, R.: Atmospheric data analysis. Cambridge Univ. Press, N. Y (1991)

  • Derber, J.C., Wu, W.S.: The use of TOVs cloud cleared radiances in the NCEP SSI analysis system. Mon. Weath. Rev. 126, 2287–2299 (1998)

    Article  Google Scholar 

  • Dudhia, J.: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two dimensional model. J. Atmos. Sci. 46, 3077–3107 (1989)

    Article  Google Scholar 

  • Hong, S.Y., Lim, J.O.J.: The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorological Soc. 42, 129–151 (2006)

    Google Scholar 

  • Hong, S.Y., Noh, Y., Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134, 2318–2341 (2006)

    Article  Google Scholar 

  • Huang, X.Y., Xiao, Q., Barker, D.M., Zhang, X., Michalakes, J., Huang, W., Henderson, T., Bray, J., Chen, Y., Ma, Z., Dudhia, J., Guo, Y., Zhang, X., Won, D.J., Lin, H.C., Kuo, Y.H.: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Weather. Rev. 137, 299–314 (2009)

    Article  Google Scholar 

  • Immerzeel, W.W., Petersen, L., Ragettli, S., Pellicciotti, F.: The importance of observed gradients of air temperature and precipitation for modeling runoff from a glacierized watershed in the Nepalese Himalayas. Water Resour. Res. 50, 2212–2226 (2014)

    Article  Google Scholar 

  • Ha, J.H., Lim, G.H., Choi, S.J.: Assimilation of GPS Radio Occultation Refractivity Data with WRF 3DVAR and Its Impact on the Prediction of a Heavy Rainfall Event. J. Appl. Met.Clim. 53, 1381–1398 (2014)

    Article  Google Scholar 

  • Lewis, J.M., Derber, J.C.: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus A. 37A(4), 309–322 (1985)

    Article  Google Scholar 

  • Kain, J.S.: The Kain-Fritsch convective parameterization: an update. J. Applied. Met. 43, 170–181 (2004)

    Article  Google Scholar 

  • Kumar, A., Houze, R.A., Rasmussen, K.L., Lidard, C.P.: Simulation of flash flooding storm at the steep edge of the Himalayas. J. Hydrometeorol. 15, 212–228 (2014)

    Article  Google Scholar 

  • Kumar, M.S., Shekhar, M.S., RamaKrishna, S.S.V.S., Bhutiyani, M.R., Ganju, A.: Numerical simulation of cloud burst event on august 05, 2010, over Leh using WRF mesoscale model. Nat. Hazards. 62, 1261–1271 (2012)

    Article  Google Scholar 

  • Kumar, P., Shukla, B.P., Sharma, S., Kishtawal, C.M., Pal, P.K.: A high-resolution simulation of catastrophic rainfall over Uttarakhand, India. Nat. Hazards. 80(1134), 1119–1692 (2016)

    Article  Google Scholar 

  • Liu, C., Schwartz, S., Snyder, C., Ha, S.: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev. 140, 4017–4034 (2012)

    Article  Google Scholar 

  • Lorenc, A. C., Rawlins, F.: Why does 4D-Var beat 3D-Var?. Quart. J. Roy. Meteorol. Soc.131, 613, 3247–3257 (2005)

  • Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W., Yao, T.: WRF simulation of a precipitation event over the Tibetan Plateau, China-An assessment using remote sensing and ground observations. Hydrol. Earth. Syst. Sci. 15, 1795 (2011)

    Article  Google Scholar 

  • McNally, A.P., Derber, J.C., Wu, W., Katz, B.B.: The use of TOVS level-1b radiances in the NCEP SSI analysis system. Q.J.R. Meteorol. Soc. 126, 689–724 (2000)

    Article  Google Scholar 

  • Mishra, A.K.: A study on the occurrence of flood events over Jammu and Kashmir during September 2014 using satellite remote sensing. Nat. Hazards. 78(2), 1463–1467 (2015)

    Article  Google Scholar 

  • Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A.: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated k-model for the long-wave. J. Geophy. Res. 102, 16663–16682 (1997)

  • Narasimha Rao, N.R., Shekhar, M.S., Singh, G.P.: Forecasting extreme precipitation event over Munsiyari (Uttarakhand) using 3DVAR data assimilation in mesoscale model. J. Earth. Syst. Sci. 129, 40 (2020)

    Article  Google Scholar 

  • Norris, J., Carvalho, L.M., Jones, C., Cannon, F., Bookhagen, B., Palazzi, E., Tahir, A.A.: The spatiotemporal variability of precipitation over the Himalaya: Evaluation of one year WRF Model simulation. Climate Dyn. 49, 2179–2204 (2017)

    Article  Google Scholar 

  • Orr, A., Listowski, C., Couttet, M., Collier, E., Immerzeel, W., Deb, P., Bannister, D.: Sensitivity of simulated summer monsoonal precipitation in Langtang Valley, Himalaya to cloud microphysics schemes in WRF. J.Geophys.Res.Atmos. 122, 6298–6318 (2017)

    Article  Google Scholar 

  • Parrish, D.F., Derber, J.C.: The national meteorological center’s spectral statistical-interpolation analysis system. Mon. Weather Rev. 120, 1747–1763 (1992)

    Article  Google Scholar 

  • Rasmussen, K.L.R., Houze, R.: A flash-flooding storm at the steep edge of high terrain: disaster in the Himalayas. B. Am.Meteorol. Soc. 93, 1713–1724 (2012)

    Article  Google Scholar 

  • Routray, A., Mohanty, U.C., Osuri, K.K., Kar, S.C., Niyogi, D.: Impact of satellite radiance data on simulations of bay of Bengal tropical cyclones using the WRF-3DVAR modeling system. IEEE. Trans.Geosci. Remote. Sens. 54(4), 2285–2303 (2016)

    Article  Google Scholar 

  • Sikka, D.R., Ray, K., Chakravarthy, K., Bhan, S.C., Tyagi, A.: Heavy rainfall in the Kedarnath valley of Uttarakhand during the advancing monsoon phase in June 2013. Curr. Sci. 109, 2353–2361 (2015)

    Google Scholar 

  • Shekhar, M.S., Chand, H., Kumar, S., Ganju, A.: Climate-change studies in western Himalaya. Ann. Glaciol. 51, 105–112 (2010)

    Article  Google Scholar 

  • Shekhar, M.S., Pattanayak, S., Mohanty, U.C., Paul, S., Kumar, M.S.: A study on the heavy rainfall event around Kedarnath area (Uttarakhand) on 16 June 2013. J. Earth. Syst. Sci. 124(7), 1531–1544 (2015)

    Article  Google Scholar 

  • Shi, J.J., Tao, W.-K., Matsui, T., Cifelli, R., Hou, A., Lang, S., Tokay, A., Wang, N.-Y., Peters-Lidard, C., Skofronick-Jackson, G., Rutledge, S., Petersen, W.: WRF simulations of the 20–22 January 2007 snow events over eastern Canada: comparison with in situ and satellite observations. J. Appl. Meteor. Climatol. 49, 2246–2266 (2010)

    Article  Google Scholar 

  • Thepaut, J.N., Courtier, P., Belaud, G.: Lematre,G.: Dynamical structure functions in a four-dimensional variational assimilation: A case study. Quart. J. Roy. Meteorol. Soc. 122(530), 535–561 (1996)

    Article  Google Scholar 

  • Wee, T.K., Kuo, Y.H., Bromwich, D.H., Monaghan, A.J.: Assimilation of GPS radio occultation refractivity data from CHAMP and SAC-C missions over high southern latitudes with MM5 4DVAR. Mon. Wea. Rev. 136, 2923–2944 (2008)

  • Whiteman, C.D.: Mountain meteorology: fundamentals and applications. Oxford University Press. (2000)

  • Wilks, D.S.: Statistical Methods in the Atmospheric Sciences, 3rd edn, p. 676. Elsevier (2011)

  • Zapotocny, T.H., Jung, J.A., Marshall, J.F.L., Treadon, R.E.: A two-season impact study of four satellite data types and rawinsonde data in the NCEP global data assimilation system. Weather Forecast. 23, 80–100 (2008)

    Article  Google Scholar 

  • Zhou, H., Gómez-Hernadez, J.J., Hendricks Franssen, H.J., Li, L.: An approach to handling nongaussianity of parameters and state variables in ensemble kalman filtering. Adv. Water. Re-sour. 34, 844–864 (2011)

    Article  Google Scholar 

  • Zhang, X., Huang, X.Y., Liu, J.: Development of an efficient regional four-dimensional variational data assimilation system for WRF. J. Atmos. Ocean. Technol. 31(12), 2777–2794 (2014)

    Article  Google Scholar 

  • Zhang, X., Huang, X.Y., Pan, N.: Development of the upgraded tangent linear and adjoint of the Weather Research and Forecasting (WRF) Model. J. Atmos. Ocean. Technol. 30(6), 1180–1188 (2013)

    Article  Google Scholar 

Download references

Acknowledgments

Authors are thankful to Director, SASE for providing full support and NCAR for using the WRF model. The authors would also like to acknowledge the NCEP GFS 0.25 Degree Global Forecast data used here as the initial and boundary conditions for the model archived from the Research Data Archive (RDA), National Center for Atmospheric Research (NCAR). NCAR is sponsored by the National Science Foundation (NSF), USA. TRMM and GPM used in this study are produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.

Author information

Authors and Affiliations

  1. Snow and Avalanche Study Establishment (SASE), DRDO, Chandigarh, India

    Narasimha Rao Nalamasu & M S Shekhar

  2. Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, India

    GP Singh

Authors
  1. Narasimha Rao Nalamasu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M S Shekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. GP Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Narasimha Rao Nalamasu.

Additional information

Responsible Editor: Dong-Hyun Cha.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix −1

1.1 Contingency Table (2 X 2)

 

Observed

Forecasted

 

Yes

No

 

Yes

Hits (a)

False Alarm (b)

(a + b) Forecasted Yes

No

Misses (c)

Correct negatives (d)

(c + d) Forecasted No

 

(a + c) Observed Yes

(b + d) Observed No

Total (N)

figure a

Statistical Scores.

1. Accuracy (Acc)

Acc = \( \frac{a+d}{N}=\frac{Correct\ Forect}{Total\ Events.} \)

Range: 0 to 1. Perfect score: 1.

2. Bias Score (Bias) =\( \frac{a+b}{a+c}=\frac{Forecasted\ events}{Observed\ Events} \)

Range: B = 1 Unbiased, B > 1 under Forecast, B < 1 Over Forecast

3. Probability of Detection (POD)

\( \left( POD=\right)\frac{a}{a+c}=\frac{Hits}{Observed\kern0.5em Events\kern0.5em \left(\mathrm{yes}\right)} \)

Range: 0 to 1. Perfect score: 1.

4. False Alarm Ratio (FAR)

\( \left( FAR=\right)\frac{b}{a+b}=\frac{False\kern0.5em Alarm}{Forecasted\kern0.5em Events\kern0.5em \left(\mathrm{yes}\right)} \)

Range: 0 to 1. Perfect score: 0.

5. Equitable Threat Score (Gilbert Skill Score; ETS)

hits_random = HR = \( \frac{\left(a+c\right)\left(a+b\right)}{N} \)

ETS = \( \frac{a- HR}{\left(\left(a+b+c\right)- HR\right)} \)

Range: −1/3 to 1, 0 indicates no skill. Perfect score: 1.

6. Heidke Skill Score (HSS)

\( \left( HSS=\right)\frac{2\ast \Big(\left(a\ast \mathrm{d}\right)-\left(\mathrm{b}\ast \mathrm{c}\right)}{\left(\left(a+c\right)\ast \left(\mathrm{a}+\mathrm{b}\right)\ast \left(\mathrm{b}+\mathrm{d}\right)\right)} \)

Range: −1 to 1, 0 indicates no skill. Perfect score: 1.

7. Mean Absolute Error (MAE)

\( \left( MAE=\right)\frac{1}{N}{\sum}_{i=1}^N\mid {F}_i-{O}_i\mid \)

8. Root Mean Square Error (RMSE)

\( \left( RMSE=\right)\sqrt{\frac{1}{N}{\sum}_{i=1}^N{\left({\mathrm{F}}_{\mathrm{i}}-{\mathrm{O}}_{\mathrm{i}}\right)}^2} \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nalamasu, N., Shekhar, M.S. & Singh, G. Performance of 4D-Var Data Assimilation on Extreme Snowfall Forecasts over the Western Himalaya Using WRF Model. Asia-Pacific J Atmos Sci 57, 555–571 (2021). https://doi.org/10.1007/s13143-020-00216-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13143-020-00216-z

Keywords

Search

Navigation