REMOTE SENSING OF PRECIPITATION IN A WARMING CLIMATE CLEMENS - - PowerPoint PPT Presentation

▶
remote sensing of precipitation in a warming climate
SMART_READER_LITE
LIVE PREVIEW

REMOTE SENSING OF PRECIPITATION IN A WARMING CLIMATE CLEMENS - - PowerPoint PPT Presentation

Oct 25, 2022 35 likes •337 views

CCC CONFERENCE COLOGNE 2018 REMOTE SENSING OF PRECIPITATION IN A WARMING CLIMATE CLEMENS SIMMER METEOROLOGICAL INSTITUTE CONTENT Precipitation as element of the global water cycle Special characteristics of precipitation as a climate

slide-1
SLIDE 1

CCC CONFERENCE COLOGNE 2018

REMOTE SENSING OF PRECIPITATION IN A WARMING CLIMATE

CLEMENS SIMMER METEOROLOGICAL INSTITUTE

slide-2
SLIDE 2

CONTENT

  • Precipitation as element of the global water cycle
  • Special characteristics of precipitation as a climate variable
  • Current and predicted trends in precipitation
  • Monitoring of precipitation – ground‐based and from space
  • Quality of climate models simulating precipitation
  • PROM – a new initiative combining models and remote sensing for improved

precipitation monitoring and simulation

  • Conclusions & Outlook

CCC Conference, April 19, 2018, Cologne 2

slide-3
SLIDE 3

480 1066 746 40 40 1176

Observations from Baumgartner and Reichel, 1975 blue: mm/year red: W/m2 black: in 1000 km3/year

58 37 92 83 111 71 425 385

THE WATER CYCLE IN NUMBERS

3

water vapor transport precipitation precipitation evapo‐ transpiration evapo‐ transpiration evapo‐ transpiration

  • cean

land infiltration groundwater flow lake melt‐ water river back flow

Uncertainties of the individual components are between 5 and 10%. Precipitation amounts to about 1m per year on average

slide-4
SLIDE 4

SPECIAL CHARACTERISTICS OF PRECIPITATION AS A CLIMATE VARIABLE

  • Precipitation is not a continuous variable ‐ but an event.
  • Its impact is more dependent on the type of event – e.g. its extremeness ‐ than on average

measures.

  • Typical measures besides mean and variance:

− Wet day/hour/… occurrence and duration − Mean intensities − Frequencies of exceedance or change of return levels

CCC Conference, April 19, 2018, Cologne 4

slide-5
SLIDE 5

CPEX-LAB: CPEX-LAB: 3D COMPOSIT RADARS BONN UND JÜLICH REFLECTIVITY

  • 4. JUNI 2016

MESZ = UTC + 2 hours

slide-6
SLIDE 6

5 (2)-HOUR SUM OVER WACHTBERG JUNE 4, 2016

CCC Conference, April 19, 2018, Cologne 6

slide-7
SLIDE 7

OBSERVED CHANGES IN PRECIPITATION ARE EXTREMELY INHOMOGENEOUS

CCC Conference, April 19, 2018, Cologne 7

(IPPC AR5)

slide-8
SLIDE 8

CLIMATE PREDICTIONS UNTIL END OF CENTURY

Predicted precipitation changes are inhomogeneous and differ between models for stronger anthropogenic forcings.

CCC Conference, April 19, 2018, Cologne 8

Change in average precipitation (1986‐2005) to (2081‐2100) RCP 2.6 RCP 8.5

slide-9
SLIDE 9

CENTRAL EUROPE MEAN SEASONAL PRECIPITATION

CCC Conference, April 19, 2018, Cologne 9

Today ca. 2050 ca. 2090 Summer Winter

 Less precipitation in summer  In winter small changes in the next 30 years but increases by up to 30% towards the end of the century, except the Mediterranean

(from Knist et al. 2018)

slide-10
SLIDE 10

PROJECTED CHANGES IN EXTREME PRECIPITATION

CCC Conference, April 19, 2018, Cologne 10

Today ca. 2050 ca. 2090

Extreme hourly precipitation (upper 1/1000) increase especially in summer, but later also in winter

(from Knist et al. 2018) Summer Winter

slide-11
SLIDE 11

HOW DO GAUGES MONITOR EXTREMES?

CCC Conference, April 19, 2018, Cologne 11

slide-12
SLIDE 12

LAND PRECIPITATION UNCERTAINTIES FROM DIFFERENT DATA SETS

CCC Conference, April 19, 2018, Cologne 12

(from Trenberth et al., 2014, Nature Climate Change)

slide-13
SLIDE 13

UNCERTAIN EUROPEAN TRENDS 1968 - 2010 IN MM

U Del.

CCC Conference, April 19, 2018, Cologne 13

GPCP CRU

GPCP – U Del.

CRU, GPCC, U Del agree in wetter North/dryer South Europe over past 4 decades. Differences between two sources (bottom right) are in the same order of magnitude as the estimated changes (same(!) color bar used for changes and differences)

slide-14
SLIDE 14

REMOTE SENSING OF PRECIPITATION

CCC Conference, April 19, 2018, Cologne 14 (from Encyclopediea of Atmospheric Science, 2002, S. 1973)

− Different sensors deployed on an aircraft flying over a hurricane − Only radar remote sensing detects precipitation − VIS, IR and passive microwaves provide only very indirect information

slide-15
SLIDE 15

COMPARISON OF SEVERAL PRECIPITATION DATA SETS

CCC Conference, April 19, 2018, Cologne 15

https://climatedataguide.ucar.edu/sites/default/files/styles/ node_lightbox_display/public/key_figures/climate_data_set/ PRECIP_TimeSeries.png?itok=H‐HW0No4

Over oceans large differences between TRMM

  • bservations and other satellite estimates

Over land products correlate much better due to gauges.

slide-16
SLIDE 16

GPCP1DD AGAINST THE PURE GAUGE-BASED E-OBS DATA SET

CCC Conference, April 19, 2018, Cologne 16

winter summer

(Lockhoff et al. 2014)

slide-17
SLIDE 17

THE GLOBAL PRECIPITATION MEASUREMENT (GPM) MISSION LAUNCHED ON FEB 28, 2014

CCC Conference, April 19, 2018, Cologne 17

NASA/JAXA contribute Core Satellite - a dual- frequency radar & - a passive microwave imager with high frequency capabilities Constellation radiometers from any agency are calibrated by the core satellite product to achieve a more frequent sampling.

slide-18
SLIDE 18

CLOUDS AND PRECIPITATION EXPLORATION LABORATORY - GPM VALIDATION SITE

CCC Conference, April 19, 2018, Cologne 18

slide-19
SLIDE 19

CPEX-LAB – GPM COMPARISONS (1)

CCC Conference, April 19, 2018, Cologne 19

Overpass : 2014.10.07 – 02:35:00 UTC

Comparison with RADOLAN reflectivities

slide-20
SLIDE 20

CPEX-LAB – GPM COMPARISONS (2)

CCC Conference, April 19, 2018, Cologne 20

3 years RADOLAN - GPM

slide-21
SLIDE 21

CPEX-LAB – GPM COMPARISONS (3)

CCC Conference, April 19, 2018, Cologne 21

slide-22
SLIDE 22

UNCERTAINTY OF CMIP MODELS IN REPRESENTING PRECIPITATION

CCC Conference, April 19, 2018, Cologne 22

− for models 10‐year running means − 22% spread between the models − Models overestimate precipitation

(from Trenberth et al. 2014, Eumetsat Climate Symposium)

slide-23
SLIDE 23

TREND IN OBSERVATIONS AND MODELS FOR THE TROPICS

CCC Conference, April 19, 2018, Cologne 23

Liu & Allan (2013)

from gauges satellites models wet areas dry areas

slide-24
SLIDE 24

PRECIPITATION REQUIRES HIGH MODEL RESOLUTIONS

3 km resolution improves the daily cycle and the intensity distribution of precipitation compared to 12 km resolution simulations.

CCC Conference, April 19, 2018, Cologne 24

(from Knist et al. 2018)

slide-25
SLIDE 25

INCREASE OF EXTREME PRECIPITATION WITH NEAR-SURFACE TEMPERATURE

March 19, 2018 Extreme Niederschläge ‐ heute und morgen 25

Clausisus-Clapeyron Scaling (~7%/°C) Stratiform precipitation Super-C-C Skalierung convective precipitation Decrease due to missing moisture at very high temperatures

mm/h 100 10 1 Daily mean near‐surface temperature °C 99% Quantile od hourly precipitation

(Sebastian Knist)

slide-26
SLIDE 26

PRIORITY PROGRAMME SPP 2115

Fusion of Radar Polarimetry and Numerical Atmospheric Modelling Towards an Improved Understanding of Cloud and Precipitation Processes

Polarimetric Radar Observations meet Atmospheric Modelling (PROM)

coordinated by Silke Trömel, Johannes Quaas, Susanne Crewell, and Clemens Simmer

CCC Conference, April 19, 2018, Cologne 26

By courtesy of A. Ryzhov and A. Khain

slide-27
SLIDE 27

MOTIVATION FOR PROM

CCC Conference, April 19, 2018, Cologne 27

  • Cloud and precipitation processes are still the main

source of uncertainties in weather prediction and climate change projections since decades

  • One

reason are missing

  • bservations,

which constrain the models

  • Since March 2015 Germany is covered by

16 state‐of‐the‐art polarimetric C‐band radars

  • CPEX‐Lab (JOYCE) and other supersites embedded in

polarimetric radar network provide ideal data base for SPP‐PROM

slide-28
SLIDE 28

POLARIMETRIC SIGNATURES OF MICROPHYSICAL PROCESSES

CCC Conference, April 19, 2018, Cologne 28

ZH ZH ZDR ZH ZDR ⍴HV KDP

slide-29
SLIDE 29

CONCLUSIONS

  • Precipitation is a difficult climate variable for observation and modeling due to its

event‐like multi‐scale nature.

  • Climate warming intensifies the water cycle and thus precipitation and its

extremes.

  • Radar methods give the best results for area covering quantitative precipitation
  • estimation. Other satellite observations are better used by assimilation into

atmospheric models i.e. in the form of reanalyses.

  • Climate models need to be run at convection‐permitting resolution to correctly

simulate the scaling of precipitation with near‐surface temperature, which is a prerequisite for predicting impact‐oriented precipitation quantities like extremes.

CCC Conference, April 19, 2018, Cologne 29

slide-30
SLIDE 30

OUTLOOK

  • Quantitatively observing precipitation amounts is a “mission impossible”; its

quantification can only be achieved by combining models with observations via model improvements and data assimilation.

  • Polarimetric radar observations will provide the strongest constraints on micro

and macrophysical precipitation processes.

  • Radar polarimetry from space should be explored to better monitor precipitation

processes over the whole globe.

CCC Conference, April 19, 2018, Cologne 30