Clear sky Net Surface Radiative Fluxes

over Rugged Terrain from Satellite Measurements

Tianxing Wang (txwang.rs@gmail.com)

Guangjian Yan (gjyan@bnu.edu.cn)

Xihan Mu (muxihan@163.com)

Ling Chen (chenling8247@126.com)

Beijing Normal University

Outline of the presentation Background

Methods

Results and discussion

Summary

Background Net surface radiative fluxes, including both net shortwave

(0.3~3m) and longwave (3~50m) radiative fluxes, are the driving force for the surface energy balance at the interface between the surface and the atmosphere

Net radiative fluxes are key input parametersfor most land models, such as GCM, hydrologyand energy models, etc.

http://serc.carleton.edu/eslabs/index.html

Thus, currently estimation of net surface fluxes is one of the hottest research issues in the field of global climate change.

LimitationsMost work focuses on the derivation of downwelling SW and upwelling LW radiation, the effective methods for directly estimating land surface net radiation are highly needed, so that the error propagation can be avoided

Almost all current researches ignore the topographic effect over the rugged terrain areas which account for about 2/3 of the global land

To date, the available radiative fluxes products (e.g., ISCCP, GEWEX,CERES) from remotely sensed data are spatially too coarse to meet the requirements of land applications

Methodologies

Fluxes over horizontal surfaces using Artificial Neuron Network

Short wave topographic

radiation model

Fluxes over rugged terrain

MODIS L1B/MOD03/MOD07/ MOD35/MOD11/MOD04 Fluxes over

horizontal surfaces (SW & LW)

Topographic modeling

Fluxes over rugged terrain

(SW & LW)

Longwave topographic

radiation model

Correctingterrain shading

Reasons for using ANN

The ANN model can accept more input variables and output more desired quantities

It has been attempted by many researches in the field of radiative flux budget proving its feasibility in such topic

It’s convenient to couple the multi-output of ANN with the topographic model for retrieving fluxes over rugged area

Topology and training over 50,000 samples simulated using MODTRAN4 single-hidden layer feed-forward network BP training algorithm

Inputs of ANN Outputs of ANN

Altitude

Downward radiative flux

Direct solar flux

Net SW radiative flux

Solar zenith angle

Viewing zenith angle

Aerosol optical depth

Water vapor index

MODIS radiances of band1~7

Moisture profile

Shortwave ANN modelLongwave ANN model

Inputs of ANN Outputs of ANN

Altitude

LW downward radiative flux

Surface emitted radiative flux

Net LW radiative flux

Viewing zenith angle

3 water vapor indices

MODIS radiances of band 20,22,23,27~29 and 31~33

Temperature profiles

Moisture profiles

Validation of the ANN models Two years of 2008~2009 in situ data are collected as reference

from seven U.S. SurfRad sites under clear sky

Validation results of the ANN models

SurfRad sitesFrom :http://www.srrb.noaa.gov/surfrad/

The maximum root mean square errors (RMSE) of ANN models are less than 45W/m2 (watts per square meter) and 25W/m2

for net SW and LW radiative fluxes, with the average biases are less than 15 W/m2 and 5W/m2, respectively. These accuracies are better than the existing algorithms showing the effectiveness of the ANN models.

Topographic radiation models

Terrain shading (a), resulting in :

Flux contribution from the around sloped terrain (b)

Sky-view ratio (c)

Key factors affecting radiative fluxes over rugged terrain

zero solar direct radiation

lower LST due to shadows

Dubayah, R. and S. Loechel (1997)

ShadowTerrain

means the overlaying sphere may be obstructed by terrain, in this situation the sky-view-ratio is less than 1

Topographic radiation models (SW)

21

0

cos cosNP M P P

d sky s dirPs sMP

net rugged netsky dir

L T T dSV F FrF F

F F

0 0/0

sdir s dir sF F S e

ssky d skyF V F

21

cos cosNP M P P

refP MP

L T T dSFr

Solar direct flux:

Sky diffused flux:

Reflected from around terrain:

Net flux over rugged terrain:

Topographic radiation models (LW)

Surface emitted flux:

Sky emitted flux:

Emitted from around terrain:

Net flux over rugged terrain:

( , )lemi emiF f LST F

llw d lwF V F

_ 21

cos cosNP M P P

emi aroundP MP

L T T dSFr

21

cos cos ( , )N

l P M P Pd lw emi

P MPl lnet rugged netl

lw emi

L T T dSV F f LST Fr

F FF F

Similarly, by considering the three factors, a LW topographic radiation model is also suggested。 This model is more complex compared to the SW model, since it relate to LST in shadow area and the broadband emissivity etc.

Correction for terrain shading

Seven shading situationsIt should be noted that, all inputs in the SW and LW topographic radiation models are the unobstructed fluxes, thus, the terrain shading of the outputs of ANN models need to be removed before incorporated in these models. This figure shows the seven shading situations， including A B C D

Correction for terrain shading

10

1

cos( )[ *cos( )]i

Mo

correct i ANNi

F P F

1

_ 0

_ _ 0

1]* 1( )

sky ANNcorrect shade ANN

sky ANN dir ANN

F FF S F

F F F

1 1 1

1 1

cos( ) cos( )[ cos( ) cos( )]i j

N Mv v

shade i ji j

S s s

1 1 1

1

cos( ) cos( )[ cos( ) cos( )]i i

Mv v

i i ii

P s s

1

_ _

_

( )1]* 1

( )diff lw emi shw t

correct shade ANNdiff lw emi t

F F FF S F

F F F

Correction for terrain shading in Shortwave band:

Correction for terrain shading in Longwave band:

These are the correcting formulas for those seven situations, the details of these variables can be found in the paper.

Results and discussion MODIS data collected on November 4, 2009 over Tibet

Plateau, a typical region for terrain undulation and climate change research, are selected as our case study

Net SW surface radiative fluxes for considering (left) and neglecting (right) the topographic effect

the terrain texture of the topographically corrected map is rather obvious

the variation of the fluxes due to the terrain undulation is much wider than that of fluxes neglecting the topographic effect

fluxes estimated by assuming a horizontal surface are difficult to reflect the true status of surface radiation budget in terms of both spatial distribution and specific flux values

Results and discussion

Net LW surface radiative fluxes for considering (left) and neglecting (right) the topographic effect

although the terrain texture of the topographically corrected map is not as obvious as that of SW , they can also be visually felled

the variation of the fluxes due to the terrain undulation is much wider than that of fluxes neglecting the topographic effect

The white spots in the left image correspond to the shadowed area, where the LST is low, thus the net LW fluxes are relatively high

Total net surface radiative fluxes for considering (left) and neglecting (right) the topographic effect

Results and discussion

Because the net SW fluxes poses a larger magnitude of total fluxes, thus the spatial distribution of the total net fluxes are highly in line with the distribution of net SW fluxes

it will attribute great errors to the estimated fluxes if the terrain undulation effect is not taken into account over rugged terrain area. The errors can reach up to 100 W/m2, and even larger for SW fluxes.

Summary Two ANN models have been developed in this paper, with

which the net surface SW and LW radiative fluxes over horizontal surface can be directly retrieved with better accuracy

Coupling the outputs of ANN models, the corresponding SW and LW topographic radiation models are also proposed

The results show that the ANN models suggested here are rather effective, and the topographic effect on the net surface fluxes is so significant that the assumption of horizontal surface is not applicable over rugged terrain

Thanks for your attention!