Coastal Ocean Wave Height Assessment

In the past we have demonstrated how to perform a CSW catalog search with OWSLib, and how to obtain near real-time data with pyoos. In this notebook we will use both to find all observations and model data in a specified box and return significant wave height.

This workflow derived from an example to advise swimmers of the annual Boston lighthouse swim of the Boston Harbor water temperature conditions prior to the race. For more information regarding the workflow presented here see Signell, Richard P.; Fernandes, Filipe; Wilcox, Kyle. 2016. "Dynamic Reusable Workflows for Ocean Science." J. Mar. Sci. Eng. 4, no. 4: 68.

In [1]:
import warnings

# Suppresing warnings for a "pretty output."

This notebook is quite big and complex, so to help us keep things organized we'll define a cell with the most important options and switches.

Below we can define the date, bounding box, phenomena SOS and CF names and units, and the catalogs we will search.

In [2]:
%%writefile wave_config.yaml

# Specify a YYYY-MM-DD hh:mm:ss date or integer day offset.
# If both start and stop are offsets they will be computed relative to at midnight.
    start: 2016-12-25 00:00:00  # -5
    stop:  2017-01-22 00:00:00  # +4

run_name: 'latest'

    bbox: [-71.5, 41.50, -70.00, 43.6]     # Gulf of Maine
    #bbox: [-71.3, 42.03, -70.57, 42.63]     # Boston Harbor region
    #bbox: [-81, 27, -79.6, 28.8]   # East Florida shelf
    #bbox: [-74.5, 40, -72., 41.5]   # New York Bight

    crs: 'urn:ogc:def:crs:OGC:1.3:CRS84'

sos_name: 'waves'

    - sea_surface_wave_significant_height
    - sea_surface_wind_wave_significant_height

units: 'm'

Overwriting wave_config.yaml

We'll print some of the search configuration options along the way to keep track of them.

In [3]:
import os
import shutil
from datetime import datetime
from ioos_tools.ioos import parse_config

config = parse_config('wave_config.yaml')

# Saves downloaded data into a temporary directory.
save_dir = os.path.abspath(config['run_name'])
if os.path.exists(save_dir):

fmt = '{:*^64}'.format
print(fmt('Saving data inside directory {}'.format(save_dir)))
print(fmt(' Run information '))
print('Run date: {:%Y-%m-%d %H:%M:%S}'.format(datetime.utcnow()))
print('Start: {:%Y-%m-%d %H:%M:%S}'.format(config['date']['start']))
print('Stop: {:%Y-%m-%d %H:%M:%S}'.format(config['date']['stop']))
print('Bounding box: {0:3.2f}, {1:3.2f},'
      '{2:3.2f}, {3:3.2f}'.format(*config['region']['bbox']))
Saving data inside directory c:\users\rsignell\documents\github\coastal_wave_heights\latest
*********************** Run information ************************
Run date: 2017-11-13 19:27:52
Start: 2016-12-25 00:00:00
Stop: 2017-01-22 00:00:00
Bounding box: -71.50, 41.50,-70.00, 43.60

We already created an OWSLib.fes filter before. The main difference here is that we do not want the atmosphere model data, so we are filtering out all the GRIB-2 data format.

In [4]:
In [5]:
def make_filter(config):
    from owslib import fes
    from ioos_tools.ioos import fes_date_filter
    kw = dict(wildCard='*', escapeChar='\\',
              singleChar='?', propertyname='apiso:AnyText')

    if len(config['cf_names']) > 1:
        or_filt = fes.Or([fes.PropertyIsLike(literal=('*%s*' % val), **kw)
                      for val in config['cf_names']])
        or_filt = fes.PropertyIsLike(literal=('*%s*' % config['cf_names'][0]), **kw)      

    not_filt = fes.Not([fes.PropertyIsLike(literal='GRIB-2', **kw)])

    begin, end = fes_date_filter(config['date']['start'],
    bbox_crs = fes.BBox(config['region']['bbox'],
    filter_list = [fes.And([bbox_crs, begin, end, or_filt, not_filt])]
    return filter_list

filter_list = make_filter(config)

In the cell below we ask the catalog for all the returns that match the filter and have an OPeNDAP endpoint.

In [6]:
from ioos_tools.ioos import service_urls, get_csw_records
from owslib.csw import CatalogueServiceWeb

dap_urls = []
print(fmt(' Catalog information '))
for endpoint in config['catalogs']:
    print('URL: {}'.format(endpoint))
        csw = CatalogueServiceWeb(endpoint, timeout=120)
    except Exception as e:
    csw = get_csw_records(csw, filter_list, esn='full')
    OPeNDAP = service_urls(csw.records, identifier='OPeNDAP:OPeNDAP')
    odp = service_urls(csw.records, identifier='urn:x-esri:specification:ServiceType:odp:url')
    dap = OPeNDAP + odp

    print('Number of datasets available: {}'.format(len(csw.records.keys())))

    for rec, item in csw.records.items():
    if dap:
        print(fmt(' DAP '))
        for url in dap:

# Get only unique endpoints.
dap_urls = list(set(dap_urls))
********************* Catalog information **********************
Number of datasets available: 20
B01 Sbe37 - CTD
Coupled Northwest Atlantic Prediction System (CNAPS)
Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near LOWER COOK INLET, AK from 2016/12/16 00:00:00 to 2017/11/12 18:09:51.
Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near OCEAN STATION PAPA from 2017/10/05 14:00:00 to 2017/11/12 17:41:33.
Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near SCRIPPS NEARSHORE, CA from 2017/10/25 20:00:00 to 2017/11/12 18:00:37.
Directional wave and sea surface temperature measurements collected in situ by Datawell Waverider buoys located near JEFFREYS LEDGE, NH from 2008/09/11 to 2017/01/14.
Directional wave and sea surface temperature measurements collected in situ by Datawell Waverider buoys located near POINT REYES, CA from 1996/12/06 to 2017/04/05.
Directional wave and sea surface temperature measurements collected in situ by Datawell Waverider buoys located near UMPQUA OFFSHORE, OR from 2006/07/12 to 2017/04/27.
NERACOOS Gulf of Maine Ocean Array: Realtime Buoy Observations: A01 Massachusetts Bay: A01 ACCELEROMETER Massachusetts Bay
Wave measurements collected in situ by sensors located near CAPE COD BAY, MA from 2016/05/20 to 2017/10/05.
A01 Accelerometer - Waves
A01 Directional Waves (waves.mstrain Experimental)
A01 Met - Meteorology
A01 Optics - Chlorophyll / Turbidity
A01 Optode - Oxygen
A01 SBE16 - CTD Transmissivity
A01 Sbe37 - CTD
B01 Aanderaa - Realtime Surface Currents
B01 Accelerometer - Waves
B01 SBE16 - CTD Transmissivity
***************************** DAP ******************************

Number of datasets available: 2
COAWST Forecast System : USGS : US East Coast and Gulf of Mexico (Experimental)
NECOFS GOM3 Wave - Northeast US - Latest Forecast
***************************** DAP ******************************

We found some models, and observations from NERACOOS there. However, we do know that there are some buoys from NDBC and CO-OPS available too. Also, those NERACOOS observations seem to be from a CTD mounted at 65 meters below the sea surface. Rendering them useless from our purpose.

So let's use the catalog only for the models by filtering the observations with is_station below. And we'll rely CO-OPS and NDBC services for the observations.

In [7]:
from ioos_tools.ioos import is_station

# Filter out some station endpoints.
non_stations = []
for url in dap_urls:
        if not is_station(url):
    except (RuntimeError, OSError, IOError) as e:
        print('Could not access URL {}. {!r}'.format(url, e))

dap_urls = non_stations

print(fmt(' Filtered DAP '))
for url in dap_urls:
************************* Filtered DAP *************************

Now we can use pyoos collectors for NdbcSos,

In [8]:
from pyoos.collectors.ndbc.ndbc_sos import NdbcSos

collector_ndbc = NdbcSos()

collector_ndbc.end_time = config['date']['stop']
collector_ndbc.start_time = config['date']['start']
collector_ndbc.variables = [config['sos_name']]

ofrs = collector_ndbc.server.offerings
title = collector_ndbc.server.identification.title
print(fmt(' NDBC Collector offerings '))
print('{}: {} offerings'.format(title, len(ofrs)))
******************* NDBC Collector offerings *******************
National Data Buoy Center SOS: 998 offerings
In [9]:
import pandas as pd
from ioos_tools.ioos import collector2table

ndbc = collector2table(collector=collector_ndbc,
                       col='sea_surface_wave_significant_height (m)')

if ndbc:
    data = dict(
        station_name=[s._metadata.get('station_name') for s in ndbc],
        station_code=[s._metadata.get('station_code') for s in ndbc],
        sensor=[s._metadata.get('sensor') for s in ndbc],
        lon=[s._metadata.get('lon') for s in ndbc],
        lat=[s._metadata.get('lat') for s in ndbc],
        depth=[s._metadata.get('depth') for s in ndbc],

table = pd.DataFrame(data).set_index('station_code')
depth lat lon sensor station_name
44007 None 43.525 -70.141 urn:ioos:sensor:wmo:44007::wpm1 PORTLAND 12 NM Southeast of Portland,ME
44013 None 42.346 -70.651 urn:ioos:sensor:wmo:44013::wpm1 BOSTON 16 NM East of Boston, MA
44029 None 42.523 -70.566 urn:ioos:sensor:wmo:44029::summarywav1 Buoy A01 - Massachusetts Bay
44030 None 43.181 -70.428 urn:ioos:sensor:wmo:44030::summarywav1 Buoy B01 - Western Maine Shelf
44090 None 41.840 -70.329 urn:ioos:sensor:wmo:44090::summarywav1 Cape Cod Bay, MA (221)
44098 None 42.798 -70.168 urn:ioos:sensor:wmo:44098::summarywav1 Jeffrey's Ledge, NH (160)

We will join all the observations into an uniform series, interpolated to 1-hour interval, for the model-data comparison.

This step is necessary because the observations can be 7 or 10 minutes resolution, while the models can be 30 to 60 minutes.

In [10]:
data = ndbc

index = pd.date_range(start=config['date']['start'].replace(tzinfo=None),

# Preserve metadata with `reindex`.
observations = []
for series in data:
    _metadata = series._metadata
    obs = series.reindex(index=index, limit=1, method='nearest')
    obs._metadata = _metadata

    # RPS hack on next line:  For some reason the standard_name was getting the SOS name "waves",
    # which Iris then rejects as "not a CF standard_name".   


In this next cell we will save the data for quicker access later.

In [11]:
import iris
from ioos_tools.tardis import series2cube

attr = dict(
    comment='Data from'

cubes = iris.cube.CubeList(
    [series2cube(obs, attr=attr) for obs in observations]

outfile = os.path.join(save_dir, ''), outfile)

Loop through the models, extracting time series using British Met Office "Iris" package

In [12]:
from iris.exceptions import (CoordinateNotFoundError, ConstraintMismatchError,
from ioos_tools.ioos import get_model_name
from ioos_tools.tardis import quick_load_cubes, proc_cube, is_model, get_surface

print(fmt(' Models '))
cubes = dict()
for k, url in enumerate(dap_urls):
    print('\n[Reading url {}/{}]: {}'.format(k+1, len(dap_urls), url))
        cube = quick_load_cubes(url, config['cf_names'],
                                callback=None, strict=True)
        if is_model(cube):
            cube = proc_cube(cube,
            print('[Not model data]: {}'.format(url))
        mod_name = get_model_name(url)
        cubes.update({mod_name: cube})
    except (RuntimeError, ValueError,
            ConstraintMismatchError, CoordinateNotFoundError,
            IndexError) as e:
        print('Cannot get cube for: {}\n{}'.format(url, e))
**************************** Models ****************************

[Reading url 1/3]:

[Reading url 2/3]:

[Reading url 3/3]:
Cannot get cube for:
Could not find "time" in ()

Next, we will match them with the nearest observed time-series. The max_dist=0.08 is in degrees, that is roughly 8 kilometers.

In [13]:
import iris
from iris.pandas import as_series
from ioos_tools.tardis import (make_tree, get_nearest_water,
                               add_station, ensure_timeseries, remove_ssh)

for mod_name, cube in cubes.items():
    fname = '{}.nc'.format(mod_name)
    fname = os.path.join(save_dir, fname)
    print(fmt(' Downloading to file {} '.format(fname)))
        tree, lon, lat = make_tree(cube)
    except CoordinateNotFoundError as e:
        print('Cannot make KDTree for: {}'.format(mod_name))
    # Get model series at observed locations.
    raw_series = dict()
    for obs in observations:
        obs = obs._metadata
        station = obs['station_code']
            kw = dict(k=10, max_dist=0.08, min_var=0.01)
            args = cube, tree, obs['lon'], obs['lat']
                series, dist, idx = get_nearest_water(*args, **kw)
            except RuntimeError as e:
                print('Cannot download {!r}.\n{}'.format(cube, e))
                series = None
        except ValueError as e:
            status = 'No Data'
            print('[{}] {}'.format(status, obs['station_name']))
        if not series:
            status = 'Land   '
            raw_series.update({station: series})
            series = as_series(series)
            status = 'Water  '
        print('[{}] {}'.format(status, obs['station_name']))
    if raw_series:  # Save cube.
        for station, cube in raw_series.items():
            cube = add_station(cube, station)
            cube = iris.cube.CubeList(raw_series.values()).merge_cube()
        except MergeError as e:
  , fname)
        except AttributeError:
            # FIXME: we should patch the bad attribute instead of removing everything.
            cube.attributes = {}
  , fname)
        del cube
    print('Finished processing [{}]'.format(mod_name))
 Downloading to file c:\users\rsignell\documents\github\coastal_wave_heights\latest\ 
[Water  ] PORTLAND 12 NM Southeast of Portland,ME
[Water  ] BOSTON 16 NM East of Boston, MA
[Water  ] Buoy A01 - Massachusetts Bay
[Water  ] Buoy B01 - Western Maine Shelf
[Water  ] Cape Cod Bay, MA (221)
[Water  ] Jeffrey's Ledge, NH (160)
Finished processing [SECOORA_NCSU_CNAPS]
 Downloading to file c:\users\rsignell\documents\github\coastal_wave_heights\latest\ 
[Water  ] PORTLAND 12 NM Southeast of Portland,ME
[Water  ] BOSTON 16 NM East of Boston, MA
[Water  ] Buoy A01 - Massachusetts Bay
[Water  ] Buoy B01 - Western Maine Shelf
[Water  ] Cape Cod Bay, MA (221)
[Water  ] Jeffrey's Ledge, NH (160)
Finished processing [fmrc-coawst_4_use_best]

Now it is possible to compute some simple comparison metrics. First we'll calculate the model mean bias:

$$ \text{MB} = \mathbf{\overline{m}} - \mathbf{\overline{o}}$$

In [14]:
from ioos_tools.ioos import stations_keys

def rename_cols(df, config):
    cols = stations_keys(config, key='station_name')
    return df.rename(columns=cols)
In [15]:
from ioos_tools.ioos import load_ncs
from ioos_tools.skill_score import mean_bias, apply_skill

dfs = load_ncs(config)

df = apply_skill(dfs, mean_bias, remove_mean=False, filter_tides=False)
skill_score = dict(mean_bias=df.to_dict())

# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')

And the root mean squared rrror of the deviations from the mean: $$ \text{CRMS} = \sqrt{\left(\mathbf{m'} - \mathbf{o'}\right)^2}$$

where: $\mathbf{m'} = \mathbf{m} - \mathbf{\overline{m}}$ and $\mathbf{o'} = \mathbf{o} - \mathbf{\overline{o}}$

In [16]:
from ioos_tools.skill_score import rmse

dfs = load_ncs(config)

df = apply_skill(dfs, rmse, remove_mean=True, filter_tides=False)
skill_score['rmse'] = df.to_dict()

# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')

The next 2 cells make the scores "pretty" for plotting.

In [17]:
import pandas as pd

# Stringfy keys.
for key in skill_score.keys():
    skill_score[key] = {str(k): v for k, v in skill_score[key].items()}

mean_bias = pd.DataFrame.from_dict(skill_score['mean_bias'])
mean_bias = mean_bias.applymap('{:.2f}'.format).replace('nan', '--')

skill_score = pd.DataFrame.from_dict(skill_score['rmse'])
skill_score = skill_score.applymap('{:.2f}'.format).replace('nan', '--')
In [18]:
import folium
from ioos_tools.ioos import get_coordinates

def make_map(bbox, **kw):
    line = kw.pop('line', True)
    layers = kw.pop('layers', True)
    zoom_start = kw.pop('zoom_start', 5)

    lon = (bbox[0] + bbox[2]) / 2
    lat = (bbox[1] + bbox[3]) / 2
    m = folium.Map(width='100%', height='100%',
                   location=[lat, lon], zoom_start=zoom_start)

    if layers:
        url = ''
        w = folium.WmsTileLayer(
            name='COAWST Wave Height',

    if line:
        p = folium.PolyLine(get_coordinates(bbox),
    return m
In [19]:
bbox = config['region']['bbox']

m = make_map(

The cells from [20] to [25] create a folium map with bokeh for the time-series at the observed points.

Note that we did mark the nearest model cell location used in the comparison.

In [20]:
all_obs = stations_keys(config)

from glob import glob
from operator import itemgetter

import iris
from folium.plugins import MarkerCluster

iris.FUTURE.netcdf_promote = True

big_list = []
for fname in glob(os.path.join(save_dir, '*.nc')):
    if 'OBS_DATA' in fname:
    cube = iris.load_cube(fname)
    model = os.path.split(fname)[1].split('-')[-1].split('.')[0]
    lons = cube.coord(axis='X').points
    lats = cube.coord(axis='Y').points
    stations = cube.coord('station_code').points
    models = [model]*lons.size
    lista = zip(models, lons.tolist(), lats.tolist(), stations.tolist())

df = pd.DataFrame(big_list, columns=['name', 'lon', 'lat', 'station'])
df.set_index('station', drop=True, inplace=True)
groups = df.groupby(df.index)

locations, popups = [], []
for station, info in groups:
    sta_name = all_obs[station].replace("'","")
    for lat, lon, name in zip(, info.lon,
        locations.append([lat, lon])
        popups.append('[{}]: {}'.format(name, sta_name))

MarkerCluster(locations=locations, popups=popups, name='Cluster').add_to(m)
<folium.plugins.marker_cluster.MarkerCluster at 0x113e7630>

Here we use a dictionary with some models we expect to find so we can create a better legend for the plots. If any new models are found, we will use its filename in the legend as a default until we can go back and add a short name to our library.

In [21]:
titles = {
    'coawst_4_use_best': 'COAWST_4',
    'global': 'HYCOM',
    'OBS_DATA': 'Observations'
In [22]:
from bokeh.resources import CDN
from bokeh.plotting import figure
from bokeh.embed import file_html
from bokeh.models import HoverTool
from itertools import cycle
from bokeh.palettes import Category20

from folium import IFrame

# Plot defaults.
colors = Category20[20]
colorcycler = cycle(colors)
tools = 'pan,box_zoom,reset'
width, height = 750, 250

def make_plot(df, station):
    p = figure(
    for column, series in df.iteritems():
        if not series.empty:
            if 'OBS_DATA' not in column:
                bias = mean_bias[str(station)][column]
                skill = skill_score[str(station)][column]
                line_color = next(colorcycler)
                kw = dict(alpha=0.65, line_color=line_color)
                skill = bias = 'NA'
                kw = dict(alpha=1, color='crimson')
            line = p.line(
                legend='{}'.format(titles.get(column, column)),
            p.add_tools(HoverTool(tooltips=[('Name', '{}'.format(titles.get(column, column))),
                                            ('Bias', bias),
                                            ('Skill', skill)],
    return p

def make_marker(p, station):
    lons = stations_keys(config, key='lon')
    lats = stations_keys(config, key='lat')

    lon, lat = lons[station], lats[station]
    html = file_html(p, CDN, station)
    iframe = IFrame(html, width=width+40, height=height+80)

    popup = folium.Popup(iframe, max_width=2650)
    icon = folium.Icon(color='green', icon='stats')
    marker = folium.Marker(location=[lat, lon],
    return marker
In [23]:
dfs = load_ncs(config)

for station in dfs:
    sta_name = all_obs[station].replace("'","")
    df = dfs[station]
    if df.empty:
    p = make_plot(df, station)
    marker = make_marker(p, station)



Now we can navigate the map and click on the markers to explorer our findings.

The green markers locate the observations locations. They pop-up an interactive plot with the time-series and scores for the models (hover over the lines to se the scores). The blue markers indicate the nearest model grid point found for the comparison.