Remote Sensing of the Hydrological Cycle Phil Arkin, Cooperative Institute for Climate and Satellites Earth System Science Interdisciplinary Center, University

Remote Sensing of the Remote Sensing of the Hydrological CycleHydrological Cycle

Phil Arkin, Cooperative Institute for Climate Phil Arkin, Cooperative Institute for Climate and Satellitesand Satellites

Earth System Science Interdisciplinary Center, Earth System Science Interdisciplinary Center, University of MarylandUniversity of Maryland

OutlineOutline

What is the hydrological cycle?What is the hydrological cycle? What sorts of interesting questions What sorts of interesting questions

can we ask?can we ask? What observations are available?What observations are available? What can we do with those What can we do with those

observations?observations? What do the results tell us?What do the results tell us? What do we need to do better?What do we need to do better?

What is the hydrological cycle?What is the hydrological cycle?(depends on what you’re talking about, of course)(depends on what you’re talking about, of course)

For the Earth, For the Earth, it’s the it’s the reservoirs of reservoirs of water and the water and the transfers transfers among themamong them

It matters to It matters to the climate the climate because of because of water’s ability water’s ability to transfer heat to transfer heat in a latent in a latent statestate

It matters to It matters to people because people because precipitation is precipitation is the original the original source of source of almost all fresh almost all fresh water we usewater we use(From UCAR web site)

Vertically integrated water Vertically integrated water balance equation for the balance equation for the

atmosphereatmosphere

- liquid and solid water small compared to vapor – neglected here- balance is between changes in storage (vertically integrated specific humidity or precipitable water) and horizontal convergence, evaporation and precipitation

What does this mean?What does this mean?

And what can we do with it? And what can we do with it?

Increase in water in atmosphere

=Horizontal amount coming in – Horizontal amount leaving + Evaporation from surface – Precipitation to surface

Divide atmosphere into boxesDivide atmosphere into boxes Calculate each quantity in each boxCalculate each quantity in each box Do that every few hoursDo that every few hours That should be enough to describe the That should be enough to describe the

hydrological cycle (water budget)hydrological cycle (water budget)

OutlineOutline




Research ResultsResearch Results Climate models indicate that global warming Climate models indicate that global warming

(or cooling) will be accompanied by changes (or cooling) will be accompanied by changes in water vapor and precipitation:in water vapor and precipitation: Water vapor changes to maintain roughly constant Water vapor changes to maintain roughly constant

relative humidity (about 7% per degree)relative humidity (about 7% per degree) Precipitation changes in the same direction as Precipitation changes in the same direction as

water vapor but at a slower rate (about 2-3% per water vapor but at a slower rate (about 2-3% per degree) degree)

That’s for global averages – regional changes will That’s for global averages – regional changes will varyvary

Observations show:Observations show: Global water vapor has increased recently as Global water vapor has increased recently as

temperatures have warmedtemperatures have warmed Global precipitation has increased also, but there Global precipitation has increased also, but there

is less agreement on the amount of changeis less agreement on the amount of change Just how good are the observations?Just how good are the observations? Can they tell us how good the models are?Can they tell us how good the models are?

Scientific QuestionsScientific Questions Does water vapor in the atmosphere track Does water vapor in the atmosphere track

surface temperature in the manner than surface temperature in the manner than the models predict?the models predict?

How well do we know how much How well do we know how much precipitation falls, both globally and precipitation falls, both globally and regionally, during the satellite era?regionally, during the satellite era? Why do I say “satellite era”?Why do I say “satellite era”? Where can available datasets be improved?Where can available datasets be improved?

Can we say anything about global Can we say anything about global precipitation variability prior to satellite precipitation variability prior to satellite observations?observations?

Can these observations help us check Can these observations help us check climate model simulations (such as those climate model simulations (such as those used in the Intergovernmental Panel on used in the Intergovernmental Panel on Climate Change assessments)?Climate Change assessments)?

2121stst Century Changes in Regional Century Changes in Regional PrecipitationPrecipitation

IPCC AR4 Summary for Policy Makers (Figure SPM.7)IPCC AR4 Summary for Policy Makers (Figure SPM.7)

Are projections like these realistic enough to merit action by society?

OutlineOutline




EvaporationEvaporation No actual observations of evaporation exist – not really an No actual observations of evaporation exist – not really an

observable quantityobservable quantity Relatively simple models based on parameterizations of Relatively simple models based on parameterizations of

turbulent fluxes can be used to calculate oceanic evaporationturbulent fluxes can be used to calculate oceanic evaporation Require observations of wind speed, near-surface gradient in Require observations of wind speed, near-surface gradient in

temperature/humiditytemperature/humidity Satellite-derived estimates of SST and wind speed are available and can Satellite-derived estimates of SST and wind speed are available and can

be usedbe used Over land, what’s needed is evapotranspiration (except in Over land, what’s needed is evapotranspiration (except in

deserts)deserts) In addition to wind speed, temperature and humidity, requires surface In addition to wind speed, temperature and humidity, requires surface

roughness and vegetation activityroughness and vegetation activity No good way to measure (quantitatively) some of theseNo good way to measure (quantitatively) some of these

Global evaporation/evapotranspiration datasets exist, but are Global evaporation/evapotranspiration datasets exist, but are based on global weather/climate models – confidence in their based on global weather/climate models – confidence in their details is lowdetails is low

Atmospheric Water VaporAtmospheric Water Vapor Radiosonde observations include relative humidity; Radiosonde observations include relative humidity;

combined with temperature can be used to calculate combined with temperature can be used to calculate specific humidity/water vaporspecific humidity/water vapor Poor samplingPoor sampling Significant instrumental errorsSignificant instrumental errors

Satellite observations can be used to estimate total Satellite observations can be used to estimate total column water vapor and its vertical profile – this has column water vapor and its vertical profile – this has been done to a limited degree (one dataset exists)been done to a limited degree (one dataset exists) NVAP (Randel and Vonder Haar, CSU)NVAP (Randel and Vonder Haar, CSU) 1988 – 1999 only1988 – 1999 only

Models can provide fields of water vapor based on the Models can provide fields of water vapor based on the combination of observations and forecasts through data combination of observations and forecasts through data assimilationassimilation Forecast models tend to deal with uncertainties by adjusting Forecast models tend to deal with uncertainties by adjusting

the water vapor, since the model adjusts it rapidly (thereby the water vapor, since the model adjusts it rapidly (thereby making the water vapor initial fields less useful)making the water vapor initial fields less useful)

PrecipitationPrecipitation The only direct, quantitative measurements The only direct, quantitative measurements

come from rain gaugescome from rain gauges Put a container near the surface (careful not to let Put a container near the surface (careful not to let

trees or buildings get in the way!) and catch trees or buildings get in the way!) and catch whatever rain fallswhatever rain falls

Pretty good absolute accuracy (but not Pretty good absolute accuracy (but not perfect)perfect)

Very limited spatial coverage (only where Very limited spatial coverage (only where people are, and tough to get data sometimes)people are, and tough to get data sometimes)

Both measurement and sampling errorsBoth measurement and sampling errors Wind and solid precipitation Wind and solid precipitation In mountains, gauges tend to be in In mountains, gauges tend to be in

unrepresentative locationsunrepresentative locations Tough data processing problem – wide Tough data processing problem – wide

variety of formats and mediavariety of formats and media

An Example for January 1994An Example for January 1994

Gauge-based analysis based on about 6500 gauges by Global Precipitation Climatology Centre, DWD

January 1994January 1994

Visible and/or infrared (IR)Visible and/or infrared (IR) Geostationary coverage nearly global (up to 60° latitude)Geostationary coverage nearly global (up to 60° latitude)

30 minute temporal sampling30 minute temporal sampling Highly empirical - you really don’t see anything except the tops Highly empirical - you really don’t see anything except the tops

of the cloudsof the clouds Many years (20 - 30) availableMany years (20 - 30) available Many, many examples - interestingly enough, almost any method seems Many, many examples - interestingly enough, almost any method seems

to work to some extentto work to some extent

ScatterinScatteringg

EmissionEmission

At lower frequencies, ocean At lower frequencies, ocean surface is cold and raindrops surface is cold and raindrops appear warmerappear warmer

Ocean only at presentOcean only at present Best way to estimate “warm” rain Best way to estimate “warm” rain

(not associated with an ice phase)(not associated with an ice phase) Also subject to errors from cold Also subject to errors from cold

surface water or icesurface water or ice Most direct (physically based) of Most direct (physically based) of

passive algorithms, but requires passive algorithms, but requires assumptions regarding assumptions regarding atmosphere (freezing level) and atmosphere (freezing level) and surface emissivitysurface emissivity

Above 50GHz, large ice particles Above 50GHz, large ice particles scatter radiation upwelling from scatter radiation upwelling from the surface – make storms look the surface – make storms look colder than backgroundcolder than background

Works over land as well as oceanWorks over land as well as ocean Good at detecting convective Good at detecting convective

precipitationprecipitation Not very useful over cold surface, Not very useful over cold surface,

especially ice or snowespecially ice or snow Algorithms more empirical than Algorithms more empirical than

emission, less so than IR/visible – emission, less so than IR/visible – depend on statistical relationship depend on statistical relationship between cloud ice and rain at surfacebetween cloud ice and rain at surface

At microwave frequencies (10-100GHz), clouds are nearly At microwave frequencies (10-100GHz), clouds are nearly transparenttransparent

Other satellite-derived Other satellite-derived estimatesestimates

better in principle, but more better in principle, but more difficult in practicedifficult in practice

Inversion - with adequate spectral Inversion - with adequate spectral resolution and a good radiative transfer resolution and a good radiative transfer model, vertical structure of rain/snow can model, vertical structure of rain/snow can be inferredbe inferred SSM/I since 1987, AMSU, AMSR-E, TMISSM/I since 1987, AMSU, AMSR-E, TMI Goddard Profiling Algorithm – GPROF, Goddard Profiling Algorithm – GPROF,

KummerowKummerow Radar - in principle, best by far; in Radar - in principle, best by far; in

practice, only recently possiblepractice, only recently possible TRMM, GPMTRMM, GPM

Model-derived estimates of Model-derived estimates of precipitationprecipitation

Other atmospheric observations contain Other atmospheric observations contain relevant informationrelevant information Winds, temperature, moistureWinds, temperature, moisture

Physically based dynamical models Physically based dynamical models yield precipitation in various waysyield precipitation in various ways NWP models forecast precipitationNWP models forecast precipitation Assimilation of radiances can yield cloud, Assimilation of radiances can yield cloud,

hydrometeor distributionshydrometeor distributions Best where models best - mid, maybe Best where models best - mid, maybe

high latitudeshigh latitudes

TMPA 3-Hrly CMORPH 3-Hrly

MERRA 3-Hrly MERRA 3-Hrly

First 7 days of January 2004

OutlineOutline




Relatively little until we combine the Relatively little until we combine the data to make maps and time seriesdata to make maps and time series

Through “analysis” – any process for combining different Through “analysis” – any process for combining different observations to create a field or time series with no gapsobservations to create a field or time series with no gaps

Satellite-derived estimates have complementary Satellite-derived estimates have complementary characteristics, so combination makes sensecharacteristics, so combination makes sense Geostationary infrared is more complete but has poor accuracy, Geostationary infrared is more complete but has poor accuracy,

low Earth orbit passive microwave is more accurate but has low Earth orbit passive microwave is more accurate but has sparse samplingsparse sampling

Satellite-derived estimates have biases that can be Satellite-derived estimates have biases that can be reduced/removed by adding information from rain gaugesreduced/removed by adding information from rain gauges

Since the input data are not uniformly distributed in time Since the input data are not uniformly distributed in time and space, an analysis (method for creating complete in and space, an analysis (method for creating complete in time and space fields from varying and incomplete time and space fields from varying and incomplete observations) must be used to create the final datasetobservations) must be used to create the final dataset

Analysis process can be statistical combination of inputs, Analysis process can be statistical combination of inputs, or simply a composite, or include an atmospheric model or simply a composite, or include an atmospheric model (often referred to as data assimilation)(often referred to as data assimilation)

Global Precipitation DatasetsGlobal Precipitation Datasets

• GPCP (left)/CMAP (right) mean annual cycle and global mean time series

• Monthly/5-day; 2.5° lat/long global; both based on microwave/IR combined with gauges

• Both have greater (but poorly known) errors in high latitudes

Multi-Source Analysis of Precipitation Multi-Source Analysis of Precipitation (MSAP)(MSAP)

Combines model Combines model precipitation with precipitation with microwave-based microwave-based estimatesestimates

Relies on satellite Relies on satellite estimates in estimates in tropics, reanalysis tropics, reanalysis in high latitudes, in high latitudes, mix in betweenmix in between

OutlineOutline



observations?observations? What can the results tell us?What can the results tell us? What do we need to do better?What do we need to do better?

Global AveragesGlobal Averages - do models give the same - do models give the same global means as observations?global means as observations?

TrendsTrends - models project large increases in - models project large increases in global mean temperature, accompanied with global mean temperature, accompanied with increases in water vapor and precipitation.increases in water vapor and precipitation. Do global datasets support these model results?Do global datasets support these model results?

Annual CycleAnnual Cycle - mean annual cycle of global - mean annual cycle of global temperature is substantial (much larger than 100 temperature is substantial (much larger than 100 year trends)year trends) Is it associated with changes in water vapor and Is it associated with changes in water vapor and

precipitation?precipitation?

Datasets based on observations (GPCP, CMAP) give about 2.6 Datasets based on observations (GPCP, CMAP) give about 2.6 mm/day (AR4 range is about 2.5-3.2 mm/day)mm/day (AR4 range is about 2.5-3.2 mm/day)

Data assimilation products average about 3 mm/day; also have Data assimilation products average about 3 mm/day; also have larger mean annual cycle and greater interannual variabilitylarger mean annual cycle and greater interannual variability

MSAP might eventually provide improved analyses, but MSAP might eventually provide improved analyses, but current DA systems appear to be a long way from current DA systems appear to be a long way from providing believable global precipitation productsproviding believable global precipitation products

Climate Model-Based Precipitation Climate Model-Based Precipitation Many of the models used in AR4 were also used to Many of the models used in AR4 were also used to

simulate the 20simulate the 20thth Century – precipitation from Century – precipitation from those runs can be compared to global precipitation those runs can be compared to global precipitation datasetsdatasets

These are anomalies – the models average about These are anomalies – the models average about 0.2 mm/day globally greater than the observations 0.2 mm/day globally greater than the observations

Mean annual cycle: T, P, E, WV from data Mean annual cycle: T, P, E, WV from data assimilationassimilation

Mean annual cycle: Temperature and Precipitation Mean annual cycle: Temperature and Precipitation from Observationsfrom Observations

Difference between CMAP and GPCP due to differences Difference between CMAP and GPCP due to differences over the ocean – no independent validation availableover the ocean – no independent validation available

Trends in global precipitationTrends in global precipitation Since we don’t have satellite observations before Since we don’t have satellite observations before

about 1980, we have to use the observations we about 1980, we have to use the observations we do have to make estimatesdo have to make estimates

We use modern datasets combined with historical We use modern datasets combined with historical observations from rain gauges as well as sea level observations from rain gauges as well as sea level pressure and sea surface temperature datasetspressure and sea surface temperature datasets

Shows an upward trend in oceanic precipitationShows an upward trend in oceanic precipitation

Reconstruction TrendsReconstruction Trends Tropical oceanic precipitation increases a lotTropical oceanic precipitation increases a lot Land precipitation, especially in Northern Land precipitation, especially in Northern

Hemisphere, decreases a bitHemisphere, decreases a bit

Comparison Comparison against model against model

simulations of the simulations of the 2020thth Century Century

These are smoothed These are smoothed annual averages annual averages

We computed joint We computed joint empirical orthogonal empirical orthogonal functions, which can functions, which can find the strongest find the strongest common features common features between the models between the models and observationsand observations

The trend is clear, with The trend is clear, with some similarity between some similarity between the observations and the observations and the modelsthe models

OutlineOutline




Conclusions/IssuesConclusions/Issues

Global data sets needed to describe the global Global data sets needed to describe the global hydrological cycle require a combination of theoretical hydrological cycle require a combination of theoretical (model) and observation input(model) and observation input Water vapor probably best except for trends Water vapor probably best except for trends Precipitation usable, but lots of ways we could improvePrecipitation usable, but lots of ways we could improve Evaporation dependent on model accuracyEvaporation dependent on model accuracy

Water vapor short-term variations look good; not as Water vapor short-term variations look good; not as good on longer time scalesgood on longer time scales

Precipitation variability:Precipitation variability: Trends – plausible, and consistent with modelsTrends – plausible, and consistent with models Global means – observed datasets agree with each other, but Global means – observed datasets agree with each other, but

are lower than modelsare lower than models Aspects of interannual variation (El NiAspects of interannual variation (El Niño) are realisticño) are realistic

Useful for climate diagnostic studies and model Useful for climate diagnostic studies and model verificationverification

Documents

Remote Sensing of the Hydrological Cycle Phil Arkin, Cooperative Institute for Climate and Satellites Earth System Science Interdisciplinary Center, University