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A multi-physics ensemble approach for short-term precipitation forecasts at convective permitting scales based on sensitivity experiments over southern parts of peninsular India

Abstract

The southern peninsular India is characterized by unique climatology with rainfall processes throughout the year from land–ocean contrasts. In addition, the complex terrain induces localized effects causing huge spatial and temporal variability in the observed precipitation. This study aims at evaluating the sensitivity of the high-resolution Weather Research and Forecasting (WRF) model (4 km) to multi-physics parameterizations, 3D variational data assimilation, and domain configuration, in the study domain covering southern peninsular India. Furthermore, the study focusses on the formulation of an ensemble method to improve the simulation of precipitation across seasons. A total of 120 experiments were set up across four crucial rainfall events, of varying spatial extent and duration, dominated by different rainfall generation mechanisms. The assessment of the experiments shows that the model’s cumulus and microphysics schemes have the highest impact on the location, intensity, and spread of the simulated 4-day long Quantitative Precipitation Forecasts (QPFs). Applying cumulus schemes at all domains represented the variability in the QPFs, across space and time, for the precipitation events dominated by convective activity. The cases without cumulus schemes at the convective scale domain (4 km), captured the higher intensity rains during organized cyclonic circulations in the north-east monsoon period. Hence, a 10-member multi-physics ensemble approach including members with and without cumulus parameterization at the fine resolution domain was adopted. The preliminary results demonstrate that the mean from the suggested ensemble approach (n-MPP) performed well in capturing the dynamics of QPFs across the rainfall events, as opposed to a single-member deterministic simulation and mean from larger member conventional multi-physics ensemble approach (c-MPP) without cumulus parameterization at the convective scale. The rank histogram, delta semi-variance plots, and outlier statistics at various lead times clearly showed that the suggested n-MPP was able to capture the high-intensity rainfall, increasing the spread of precipitation forecasts and consequently reducing the occurrence of outliers.

Highlights

  • Evaluated the sensitivity of a high-resolution Numerical Weather Prediction (NWP) model (4 km) with 120 experiments in generating short-term (4-day long) quantitative precipitation forecasts (QPFs).

  • The cumulus and microphysics schemes have the highest impact on the location, intensity, and spread of the simulated rainfall across events dominated by different rainfall generation mechanisms.

  • A 10-member multi-physics ensemble approach including members with and without cumulus parameterization at the fine resolution domain was able to capture the high-intensity rainfall, increasing the spread of precipitation forecasts and consequently reducing the occurrence of outliers.

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Abbreviations

3DVAR–DA:

Three-Dimensional Variational Data Assimilation

BMJ:

Betts–Miller–Janjic cumulus scheme

c-MPP:

Conventional type Multi-Physics Parameterization (MPP) ensemble consisting of 14 members from different physics schemes viz., C0, C1, C2, C3, C4, C5, C6, C7, C10, C12, C14, C16, C18 and C19

CNT:

Control Run, alternatively referred as C0 for ease of usage and use in figures

CPS:

Convective Permitting Scales

CUM:

Sub-cluster including sensitivity cases with changes in cumulus schemes

CUM-D3:

Sub-cluster including sensitivity cases with cumulus schemes implemented in Domain 3

D1:

Domain 1 as given in figure 1

D2:

Domain 2 as given in figure 1

D3:

Domain 3 as given in figure 1

DA:

Main sensitivity cluster including cases with changes in data assimilation approach

DOM:

Main sensitivity cluster including cases with changes in domain configuration

DOM1:

Sub-cluster including sensitivity cases with any single domain (no nesting) selected form the CNT run

DOM2:

Sub-cluster including sensitivity cases with any two domains (one nesting step) selected form the CNT run

EPS:

Ensemble prediction system

ERA5:

ECMWF atmospheric reanalyzes of the global climate data

FSS:

Fractional Skill Score

GAJ:

Gaja cyclone associated extreme rain event

GF:

Grell-Freitas cumulus scheme

GFS:

Global Forecast System generated initial and boundary data

GPM–IMERG:

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals data

IST:

Indian Standard Time

KF:

Kain–Fritsch cumulus scheme

KF1:

Moisture advection based trigger for KF cumulus scheme

KF2:

Relative Humidity dependent additional perturbation for the KF cumulus scheme

LAM:

Limited Area Model

MPY:

Sub-cluster including sensitivity cases with changes in microphysics schemes

MYJ:

Mellor–Yamada–Janjic (MYJ) PBL Scheme

NEMS:

North-East Monsoon Season

n-MPP:

Newly suggested Multi-Physics Parameterization (MPP) consisting of 10 members viz., C0, C5, C7, C8, C9, C11, C13, C15, C17 and C16

No-CUM:

Sub-cluster including sensitivity cases without cumulus schemes in Domain 3

NT:

New Tiedtke cumulus scheme

OCH:

Ockhi cyclone associated extreme rain event

PBL:

Sub-cluster including sensitivity cases with changes in planetary boundary layer schemes

PCS:

Parameterized Convection Scales

PHY:

Main sensitivity cluster including cases with changes in physics schemes

PM:

Pre-monsson season

QPF:

Quantitative Precipitation Forecast

S-A-L:

Structure-Amplitude-Location

SUM:

Pre-monsoon season extreme rain event

SWM:

South-West Monsoon Season extreme rain event

SWMS:

South-West Monsoon Season

WRF:

Weather Research and Forecasting model

YSU:

Yonsei University Scheme for PBL

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Acknowledgement

We would like to acknowledge the funding support from the Ministry of Water Resources, RD&GR, Govt. of India under Indian National Committee on Climate Change (INCCC) Grant no: 16/22/2016-R&D.

Author information

Affiliations

Authors

Contributions

S M Kirthiga: Conception and design of the study, data download and processing, analysis and interpretation of data, drafting of the manuscript, and approval of the final version. B Narasimhan: Conception of the study, providing resources, revising the manuscript critically for important intellectual content and approval of the final version of the manuscript and C Balaji: Conception of the study, providing resources, revising the manuscript critically for important intellectual content, and approval of the final version of the manuscript.

Corresponding author

Correspondence to C Balaji.

Additional information

Communicated by Kavirajan Rajendran

Appendices

Appendix

A.1 Formulation of FSS

Fractional skill score is helpful in determining the usefulness of a forecast for a given rainfall threshold and neighbourhood size. The higher the neighbourhood size, the FSS for same rainfall threshold will be high, since it averages spatially a larger areal extent, thus removing the errors due to spatial shift. The measure is subjective in the sense the score will depend on the selection of neighbourhood size and the rainfall threshold. In general, the FSS is derived for different rainfall thresholds and neighbourhood sizes. In this study, as mentioned in section 3.2, 5 × 5 pixels were selected as neighbourhood size (neighbourhood scale of 50 km) after testing neighbourhood scales of 20, 30, 50, and 70 km. Four different threshold levels, viz., 1 mm/3 hr, 2.5 mm/3hr, 5 mm/3hr, and 10 mm/3hr rainfall rates were selected based on the intensities of the selected events. The binary map (if the value of a pixel in the raster is above the selected threshold, the value 1 will be assigned to the pixel and vice versa) for four rainfall thresholds was derived for both observed and simulated average rainfall rate raster for every event. Later a non-overlapping 5×5 size window (50 km neighbourhood scale) was moved across the raster and the fractions within the window were calculated. The FSS for every sensitivity experiment was computed using equation (A1).

$$ {\text{FSS}} = 1 - \frac{{\frac{1}{N}\mathop \sum \nolimits_{1}^{N} \left( {P_{f} - P_{o} } \right)^{2} }}{{\frac{1}{N}\left[ {\mathop \sum \nolimits_{1}^{N} P_{f}^{2} + \mathop \sum \nolimits_{1}^{N} P_{o}^{2} } \right]}}, $$
(A1)

where \(P_{f}\) is forecast fraction, \(P_{o}\) is observed fraction, and N is the number of spatial windows in the evaluation domain.

A.2 Formulation of S–A–L

The quality measure S–A–L (as suggested by Wernli et al. 2008) has been designed to effectively assess the NWP performance of simulating single-type precipitation within a defined domain like a river basin. Furthermore, the metric does not require subjective decisions on the threshold or the size of the neighbourhood like FSS. The components of S–A–L, i.e., structure, amplitude, and location are derived based on the order of complexity which goes from A to L and then finally to S. The implementation of S–A–L involved the following steps:

  • (1) Define domain: In the study, the evaluation domain 3 was selected as the domain for the derivation of S–A–L.

  • (2) Identifying rain objects: As S–A–L is an object-based metric, the precipitation objects need to be defined. The threshold value (R*) to select the object was defined by \( R{*} = \frac{1}{15}R^{95}\), where \(R^{95} \) is the 95th percentile of all the values within the domain, calculated separately for each precipitation field considered. Binary map with 1 representing the values beyond \(R{*} \) was derived. An algorithm was formulated to identify spatially connected cells with value 1 in the binary map and was declared as rain object.

  • (3) Amplitude: A was calculated as the normalized difference of domain averaged precipitation between simulated and observed precipitation field. The values range from –2 to 2 are analogous to bias metrics (grid-based metrics). Positive amplitude values denote over-estimation, and negative values represent under-estimation of the simulated rainfall. Value 0 represents perfect prediction.

  • (4) Location: The L component comprises two additional parts. The first part (L1) accounted for the distance between the center of mass of the simulated and observed precipitation fields. L1 ranges from 0 to 1 with 0 representing no deviation of the center of mass in the simulated precipitation field from the observed. Second part (L2) was calculated as averaged distance between the center of mass of the total rainfall and the individual rain objects as computed in step 2. L2 will become 0, if both the simulated and observed precipitation fields have single rain object. In other scenarios, the range of L2 will be between 0 and 1. Thus, the value of L ranges between 0 and 2 with 0 denoting no shift from the observed field, while higher values indicating a spatial shift from actual rainfall occurrences.

  • (5) Structure: The structure component (S) compared the volume of rain objects, quantifying the size and shape of the objects. The values range between –2 and 2, with negative values signifying that the event is simulated as a small spread and peaked event. Positive values on the other hand represent a much broader or flat event simulated by the model when compared to the actual event. Values close to 0 represent closely simulated rain structures as compared to the observed.

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Kirthiga, S.M., Narasimhan, B. & Balaji, C. A multi-physics ensemble approach for short-term precipitation forecasts at convective permitting scales based on sensitivity experiments over southern parts of peninsular India. J Earth Syst Sci 130, 68 (2021). https://doi.org/10.1007/s12040-021-01556-8

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Keywords

  • Convection-permitting scales
  • multi-physics ensemble
  • physics parameterization schemes
  • WRF model sensitivity
  • southern peninsular India
  • quantitative precipitation forecasts