monsoon rainfall forecasting pankaj jain iit kanpur
TRANSCRIPT
Introduction
• Monsoon prediction is clearly of great importance for India
• One would like to make long term prediction, i.e. predict total
monsoon rainfall a few weeks or months in advance
short term prediction, i.e. predict rainfall over different locations a few days in advance
Predicting total monsoon rainfall (June-September)
• predicted by using its correlation with observed parameters
• The predictors keep changing with time • Several regression and neural network
based models are currently available• Indian Met. Dept (IMD) provides
statistical forecast in two stages, March/April May/June
No. Predictor (Period) Used for the forecasts in
1. North Atlantic Sea Surface Temperature (Dec. + Jan.)
April and June
2. Equatorial SE Indian Ocean Sea Surface Temp. (Feb. + March)
April and June
3. East Asia Mean Sea Level Pressure (Feb. + March)
April and June
4. NW Europe Land Surface Air Temperatures (Jan.)
April
No. Predictor (Period) Used for the forecasts in
5. Equatorial Pacific Warm Water Vol. (Feb.+March)
April
6. Central Pacific (Nino 3.4) Sea Surface Temperature Tendency (MAM-DJF)
June
7. North Atlantic Mean Sea Level Pressure (May)
June
8. North Central Pacific Wind at 1.5 Km above sea level (May)
June
Model
• IMD uses both linear and non-linear regression for their forecast
• use ensemble forecast large number of models are used for all possible
combinations of predictors only a few models with best skill are selected
• The forecast is the weighted average of the outcome of these models
• The model error 5% for April forecast 4% for June forecast
Short term Forecasting
• We have been interested in forecasting daily rainfall over a particular location a few days in advance.
• The government agency National Center for Medium Range Weather Forecasting (NCMRWF) provides daily forecasts, mainly to assist farmers.
Numerical Weather Prediction (NWP) Models
• Numerical Weather Prediction (NWP) models• Used to make short (1-3 days) and medium
(4-10) forecast• Navier Stokes equation is written on a
spherical grid covering the entire earth• use spherical polar coordinates • need to account for the earth’s rotation,
which makes it a non-inertial frame. This introduces fictitious Centrifugal and Coriolis force
• The variables are expanded in spherical harmonics, truncated up to a certain multipole, which determines the resolution of the grid.
• For example the current model is T254, which implies a grid size of 0.5ox0.5o
• 64 vertical levels
• 7.5 min time steps
Earth-Atmosphere System
Potential Energy
Kinetic Energy
Frictional Dissipation
Solar Radiation
unequal
heating
Long wavelength radiation
• The inputs to the model are the initial conditions obtained by observations throughout the earth
temperature,
pressure,
wind velocity,
humidity etc
as a function of position and height
However the model is severely limited: The outcome, especially rainfall, is strongly
dependent on local factors This is particularly true in tropics where the
circulation is primarily driven by convection It is unfeasible to take all local factors into
consideration in a global model The prediction may change considerably by
very small changes in the input parameters
The output of the model is the desired prediction
The input data, especially high altitude balloon data, is severely limited
Also in many regions, especially India, the data quality is often not very good
There may also exist some unknown effects. An interesting possibility is effect of galactic cosmic rays
This possibility has been studied by Tripathi et al (CE, IITK)
Variation of low-altitude cloud cover, galactic cosmic rays and total solar irradiance (1984-1994). The cosmic ray intensity
data is from Huancayo observatory, Hawaii
Carslaw et al., 2002, Science
The physical links for the correlation is subject of research.
Ion Mediated Rote
Cloud drop
Further growth
CCN
Neutral or ion clusters
Molecules Ion-inducednucleation
ThermodynamicallyStable clusters
Initialgrowthstep
Aerosol particles
Condensational growth
Particle-particle coagulation
Tripathi and Harrison, 2001; Tripathi et al, 2006
s
Charged Aerosol Collision
Two Routes to cloud modification: Charged Species is the Key!
Drop charge D
Image charge I
CONDUCTINGWATERDROP
c
CHARGEDAEROSOL
ChargedAerosolTrajectory
Modgil, Kumar, Tripathi et al., 2005, JGR
Statistical Interpretation of NWF output
• It may be better to statistically correlate the model output with observations
• This is the technique used by NCMWRF to predict daily rainfall a few days in advance at a particular station
• The rainfall at a particular station is obtained by a rain gauge
• We have been trying to determine if neural network based relationship can improve the predictability.
Jain et al 1999, Jain and Jain 2002
Number Variable Level (hPa)
1-4 Geopotential 1000,850,700,500
5-8 Temperature 1000,850,700,500
9-12 Zonal wind comp. 1000,850,700,500
13-16 Meridional wind comp. 1000,850,700,500
17-20 Vertical velocity 1000,850,700,500
21-24 Relative humidity 1000,850,700,500
25 Saturation deficit 1000-500
26 Precipitable water 1000-500
27 MSLP
Number Variable Level (hPa)
28-29 Temperature gradient 850-700,700-500
30-31 Advection of TG 850-700,700-500
32-35 Advection of temp 1000,850,700,500
36-39 vorticity 1000,850,700,500
40-43 Advection of vorticity 1000,850,700,500
44-45 thickness 1000-500
45 Horizontal water vapor flux divergence
1000-500
46 Mean relative humidity 1000-500
47 Rate of change of moist static energy
1000-500
Scale invariance in daily rainfall
• The predictability of the quantity can often be judged by its distribution function.
• If the variable shows a normal distribution then large fluctuations from the mean value are improbable
• However a power law distribution
f(x) = x
implies no characteristic scale (scale invariant)
indicates an underlying chaotic behaviour
SSM/I satellite data
A power law fits the distribution very well at low latitudes
Hourly rainfall Distribution
5S-10S
Rainfall rate in mm/hour
Fre
qu
enc
y
short term rainfall over a localized region shows a scale invariant power law distribution Jain and Jain 2002
Peters et al (2002) show this for individual events at the Baltic coast
Power law exponent as a function of the latitude f(x) = x
In tropics =1.130.14
At higher latitudes =1.3-1.6Jain and Jain, 2002
Latitude
Exp
on
ent
• It is better to define variable which we may have a better chance of predicting.
• Rather then using a single rain gauge it may be more appropriate to use the rainfall averaged over many rain gauges.
• The NWF has a grid size of order 100x100 Km. Hence its predictions should be interpreted as the average over the grid rather than for a particular location.
Predicting Daily Rainfall
• We studied daily rainfall forecast one day in advance• The following stations were considered: Delhi, Pune, Hyderabad, Bangalore,Bhubaneshwar • The output variable (y) is the daily rainfall• 47 input variables (xi), each at 9 grid points
surrounding the station. select by quadratic fitting over the 9 grid locations • 6 years data (1994-1999) from June to September for
Pune, Hyderabad, Bangalore,Bhubaneshwar For Delhi we consider data from June to August
Neural Networks Neural networks differ from statistical regression
techniques since one does not try to fit the output. By fitting we mean minimization of the error
yi is the predicted variable and y’i the measured variable.
Instead one only tries to learn the behaviour of the predictor.
Sum runs over the training set2' )( i
ii yy
We terminate the training when the error in the validation becomes minimumThen the results are checked on an independent set.
While training one has to be careful that the network is not struck in some local minima
genetic algorithms or simulated anealing
Performance Indices
We shall predict (a) the probability of rainfall and (b) the actual rainfall (actually the cube
root of rainfall)
The skill of the model is tested by suitable performance indices
A model is skillful if it performs better than persistence model
Probability of Rainfall
Performance Indices:
Ratio = number correct / total
)]()()][()([
)()()()(..
wetMwetNdryMdryN
wetMdryMwetNdryNKH
N(dry) = no. of correctly predicted dry daysM(dry) = no. of incorrectly predicted dry days
2' )(1
.. ii
i yyN
SB
Amount of RainfallCube root of Precipitation
Performance Index: Root Mean Square Error
2' )(1
ii
i yyN
RMSE
Results (Pune)
Model Network Training
error
B.S. Ratio H.K.
LR 41.81 0.164 0.769 0.485
NN
CG
42-3-1-1 42.3 0.154 0.806 0.584
NN
BP
42-4-3-1 43.0 0.151 0.785 0.532
Results (Hyderabad)
Model Network Training
error
B.S. Ratio H.K.
LR 56.9 0.233 0.620 0.195
NN
CG
42-4-4-1 50.8 0.227 0.694 0.360
NN
BP
42-3-1 57.5 0.217 0.661 0.277
Results (Bangalore)
Model Network Training
error
B.S. Ratio H.K.
LR 62.88 0.228 0.636 0.176
NN
CG
42-4-4-1 53.2 0.225 0.678 0.318
NN
BP
42-4-4-1 62.2 0.230 0.686 0.332
Results (Bhubaneshwar)
Model Network Training
error
B.S. Ratio H.K.
LR 53.9 0.219 0.644 0.240
NN
CG
42-4-4-1 54.4 0.211 0.652 0.332
NN
BP
42-4-4-1 53.8 0.209 0.669 0.344
Results (Delhi)
Model Network Training
error
B.S. Ratio H.K.
LR 36.9 0.191 0.723 0.377
NN
CG
42-1 35.2 0.198 0.723 0.382
NN
BP
42-1 42.2 0.167 0.750 0.436
Conclusions
• We find that in tropics the short term rainfall distribution follows a universal power law with exponent 1.130.14
• Predicting daily rainfall at a particular rain gauge appears to be difficult
• Neural Networks give a modest improvement over linear regression results
• We recommend that instead of a single rain gauge one should use a spatial average over many rain gauges, which gives significantly better results
Ion induced nucleation mechanism
Ion-induced nucleation mechanism. In this example, the neutral nucleation pathway is inhibited due to a barrier on the Gibbs free energy surface. Clusters smaller than the critical cluster preferentially evaporate whereas clusters larger than the critical cluster grow. The ion cluster growth is spontaneous and competes with recombination (vertical arrows). Recombination that produces a neutral particle larger than the critical cluster leads to nucleation. This process is indicated by the large arrows.
Modgil, Kumar, Tripathi et al., 2005, JGR
Freezing probability from electrical Enhancement of aerosol collection rate P, calculated as a function of particle elementry charges J. Neutral (supercooled) droplets of radii 52, 40, 32, 26 and 18 µ m are considered to collect aerosol particles of radii 0.4, 0.4, 0.5, 0.6and 0.4 µ m Respectively.
Tripathi and Harrison, , 2002
plotted as a function of ion asymmetry factor x for various aerosol radii a.N20 and N-20 are number concentrationsof aerosols carrying -20 and +20 charges respectively and Z(103 cm-3 )is the total number concentration. Horizontal line indicates the regionabove which 1 particle per cm will be present.(b) Same as (a) except for
N10 and N-10 .
20 20N N
Z
More ice formation through contact ice nucleation in cold clouds