Download - Observing Networks, Precipitation and Extreme Precipitation

Observing Networks, Precipitation and Extreme Precipitation

Paul H. WhitfieldMeteorological Service of CanadaDepartment of Earth Sciences, Simon Fraser University

Can we learn more about extremes [and climatology in general] from networks of stations rather than single stations?

What do we learn when the 100 year event happens at one location?

• Background

• Multiple site precipitation

• Multiple site extremes

• Pineapple Express

A scheme for precipitation measures at two locations (Toews et al. 2009)

Multi-site Precipitation

Precipitation has many types (drizzle, hail, virga, graupel).

For some meteorological & climatological applications, it can be classified into:

Convective vs. stratiform

(Based on mechanisms of genesis)

And for some other applications

(e.g., Hydrology):

Local vs. regional:

(Based on distribution and impact)

Algorithms

Let PX and PY be daily precipitation amounts at X and Y

η =PX / PY (1)

For a suitable η > 1, η ≥ η ≥ η-1 is regarded as regional

Otherwise as local

Or λ = max(PX, PY) / (PX + PY) (2)

For a suitable λ > 0.5, λ > λ is regarded as local

λ ≤ λ is regarded as regional

Algorithms

• For precipitation measures

at multiple (M) locations,

(3)

where M is number of locations with precipitation (M ≥ 2), MH ≤ M / 2,

And P represents the precipitation amount, P1 ≥ P2 ≥ ··· ≥ PM ,

For a suitable Λ > 0.5, Λ > Λ is regarded as local

Λ ≤ Λ is regarded as regional

M

mm

M

mm

H

PPM

M H

112

Applications• The Dongting Lake Basin

Subtropical monsoonal

climate, wet in summer.

• The BC Inner South Coast

Mid-latitude coastal

climate, wet in winter.

Data• Daily precipitation data from the 10 climate stations

(1979 – 2008)

• NCEP/DOE AMIP-II Reanalysis datasets

(2.5°x2.5° grid, every 6 hrs) Wet season: 9 Apr – 8 Jul (DLB-HP) and 1 Nov – 30 Jan (ISC-BC)

Dry season: 15 Nov – 13 Feb (DLB-HP) 19 Jun – 17 Sep (ISC-BC)

Multi-Location Scheme

( 0.5 ≤ Λ ≤ 1 )

All year round

DLB-HPCross plots of daily precipitation between two of five selected climate stations in using a log-log scale.

Based on Eq. (2) with λ = 0.75

• Regional events

• Local events

Regression lines

Wet Season



• Regional events

• Local events

Regression lines

Dry Season



• Regional events

• Local events

Regression lines

(Multi-Location scheme with Λ = 0.75) Composites and anomalies from climatology

1333 Local

318 Regional


777 Local

331 Regional

(Multi-Location scheme)

(with Λ = 0.75)

Climatological mean

850-hPa moisture flux & divergence

Composite local events:

Composite regional events:

All year round

ISC-BCCross plots of daily precipitation between two of five selected climate stations in using a log-log scale.


• Regional events

• Local events

Regression lines

Wet Season



• Regional events

• Local events

Regression lines

Dry Season



• Regional events

• Local events

Regression lines


1620 Local

577 Regional


1128 Local

90 Regional

(Multi-Location scheme)

(with Λ = 0.75)

Climatological mean

850-hPa moisture flux & divergence

Composite local events:

Composite regional events:

Pineapple Express?

Extremes

Multi-Location Scheme

( 0.5 ≤ Λ ≤ 1 )

At least one station p>25mm

All year round



• Regional events

• Local events

Regression lines


(Multi-Location scheme with Λ = 0.75) Composites and anomalies from climatologyAt least one station p>25mm

331 Local

182 Regional



27 Local

45 Regional

All year round



• Regional events

• Local events

Regression lines




218 Local

183 Regional



55 Local

11 Regional

Pineapple Express

Pineapple Expressa weather condition made of a jet stream of wet, warm air that reaches western North America from the Pacific Ocean, usually by way of Hawaii, and causes

heavy rainfall.

November 2006 flood, Granite Falls on the Stillaguamish River

Lake Bad Water – Death Valley Winter 2005

Pineapple Express

Mean seasonal cycles of the timing of pineapple-express circulations (bars) and the latitude of the jet stream core (maximum wind speeds at 250 hectopascals (hPa) pressure levels) north of Hawaii (curve); notice that the timing histogram has been reversed for comparison to the latitude curve.

Relations (a) between the NINO3.4 sea-surface temperature (SST) index of the El Niño-Southern Oscillation and the sum of water-vapor transports (Table 1) by pineapple-express circulations, and (b) between the Pacific Decadal Oscillation (PDO) index and the circulations. Red dots indicate (a) El Niños and (b) El Niño-like PDO years; blue dots indicate (a) La Niñas and (b) La Niña-like PDO years; green dots are neutral years. Lines are regression fits.

Exceedance probabilities for day-to-day changes in December–February discharges in the Merced River at Happy Isles, Yosemite National Park, under various circulation and precipitation conditions.

Det

tinge

r 20

04

Climate/weather questions:

• Does the distance from the jet stream affect the intensity? During events stations outside the ‘front’ should have less precipitation than those along the band. Using real observations can we detect if this is true? This needs a new way to structure data.

• Determine relative frequency of events in observations across a sample space of the climate network with some augmentation for the first 100 km south of Canada

• Effect of the Pineapple Express should both decrease with distance from the coast and have some significant effects in some regions of the interior. Is this more prevalent in some locations?

• Does the angle of transit affect intensity of precipitation [i.e. does it explain Tropical Punch?]

• Can we understand better the connection to ENSO and PDO to occurrence?

• Will these events change in a warmer climate – is there a way to tease out any part of the pineapple express variability to warmer/cooler periods/years?

Flood/streamflow questions:

• Does the distance from the jet stream affect the intensity? During events stations outside the ‘front’ should have less precipitation than those along the band. Using real observations can we detect if this is true? This needs a new way to structure data.

• Determine relative frequency of events in observations across a sample space of the climate network with some augmentation for the first 100 km south of Canada

• Effect of the Pineapple Express should both decrease with distance from the coast and have some significant effects in some regions of the interior. Is this more prevalent in some locations?

• Does the angle of transit affect intensity of precipitation [i.e. does it explain Tropical Punch?]

• Can we understand better the connection to ENSO and PDO to occurrence?

• Will these events change in a warmer climate – is there a way to tease out any part of the pineapple express variability to warmer/cooler periods/years?

Discussion questions:

• Will climate change shift the landing of Pineapple Express northward?

• Can information from multiple sites be useful in validating models?

• Can we apply EVT to multiple stations?

• How to deal with processes that are important at local scale and not resolved in GCM?

Download - Observing Networks, Precipitation and Extreme Precipitation

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